Additional Figures
Accompanying ‘Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy, an analysis in the Athero-EXPRESS Biobank.’
Sander W. van der Laan, PhD | s.w.vanderlaan@gmail.com.
2023-06-06
General Setup
Create a new analysis directory...source(paste0(PROJECT_loc, "/scripts/functions.R"))source(paste0(PROJECT_loc, "/scripts/pack01.packages.R"))
* General packages...Background
This notebook contains additional figures of the project “Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank.”.
Loading data
# load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
load(paste0(PROJECT_loc, "/20220319.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))source("/Users/slaan3/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9/scripts/local.system.R")
Create a new analysis directory...Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")Fix some variables
We need to get the ‘conventional unit’ versions of cholesterols.
AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9,
subset(AEDB.CEA, select = c("STUDY_NUMBER",
"risk614",
"LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")),
by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)Fix counts
We limit the maximum count at 100.
# ?ggpubr::ggboxplot()
# compare_means(LRCH1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
# ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup))
cat("Get summary statistics for target:\n")Get summary statistics for target:summary(AERNASE.clin.hdac9$HDAC9) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 11.00 21.00 28.32 37.00 449.00 cat("\nCount number of values > 100 for target:\n")
Count number of values > 100 for target:sum(AERNASE.clin.hdac9$HDAC9 > 100)[1] 12cat("\nSetting values > 100 for target to 100.\n")
Setting values > 100 for target to 100.AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(HDAC9_max100 = ifelse(HDAC9 > 100, 100, HDAC9))
cat("Get summary statistics for target after fixing values:\n")Get summary statistics for target after fixing values:summary(AERNASE.clin.hdac9$HDAC9_max100) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 11.00 21.00 26.71 37.00 100.00 Additional figures
Age and sex
We want to create per-age-group figures median ± interquartile range.
- Box and Whisker plot for target(s) plaque levels by sex.
- Box and Whisker plot for target(s) plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).
# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ Gender, data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("Gender"),
y = "HDAC9",
xlab = "gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Gender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Gender, data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("Gender"),
y = "HDAC9_max100",
xlab = "gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Gender.pdf"), plot = last_plot())Saving 12 x 8 in imagelibrary(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+")))
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females")))
table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$Gender)
female male
<55 11 27
55-64 43 124
65-74 58 191
75-84 37 119
85+ 4 9table(AERNASE.clin.hdac9$AgeGroupSex)
<55 females <55 males 55-64 females 55-64 males 65-74 females 65-74 males 75-84 females 75-84 males 85+ females
11 27 43 124 58 191 37 119 4
85+ males
9 Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ AgeGroup, data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9",
xlab = "Age groups (years)",
ylab = "HDAC9 (normalized expression)",
color = "AgeGroup",
palette = "npg",
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9",
xlab = "Age groups (years) per gender",
ylab = "HDAC9 (normalized expression",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup_perGender.pdf"), plot = last_plot())Saving 12 x 8 in image# ?ggpubr::ggboxplot()
compare_means(HDAC9_max100 ~ AgeGroup, data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9_max100",
xlab = "Age groups (years)",
ylab = "HDAC9 (normalized expression)",
color = "AgeGroup",
palette = "npg",
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.AgeGroup.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9_max100",
xlab = "Age groups (years) per gender",
ylab = "HDAC9 (normalized expression",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.AgeGroup_perGender.pdf"), plot = last_plot())Saving 12 x 8 in imageHypertension & blood pressure
We want to create figures of target(s) levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.
- Box and Whisker plot for target(s) plaque levels by hypertension group (no, yes)
- Box and Whisker plot for target(s) plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
systolic >= 120 & systolic <= 139 ~ "120-139",
systolic >= 140 & systolic <= 159 ~ "140-159",
systolic >= 160 ~ "160+")))
table(AERNASE.clin.hdac9$SBPGroup, AERNASE.clin.hdac9$Gender)
female male
<120 7 22
120-139 30 81
140-159 36 120
160+ 62 169Now we can draw some graphs of plaque target(s) levels per sex and hypertension/blood pressure group as median ± interquartile range.
compare_means(HDAC9 ~ SBPGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg)",
ylab = "HDAC9 (normalized expression)",
color = "SBPGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.selfreport, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.selfreport",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.drugs, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9",
xlab = "Hypertension medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.HypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9",
xlab = "Hypertension medication use per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg) by medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension per medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ SBPGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9_max100",
xlab = "Systolic blood pressure (mmHg)",
ylab = "HDAC9 (normalized expression)",
color = "SBPGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Hypertension.selfreport, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9_max100",
xlab = "Self-reported hypertension",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.selfreport",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Hypertension.drugs, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9_max100",
xlab = "Hypertension medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.HypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9_max100",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9_max100",
xlab = "Self-reported hypertension per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9_max100",
xlab = "Hypertension medication use per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)),
x = c("SBPGroup"),
y = "HDAC9_max100",
xlab = "Systolic blood pressure (mmHg) by medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "HDAC9_max100",
xlab = "Self-reported hypertension per medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imageHypercholesterolemia & LDL levels
We want to create figures of target(s) levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.
- Box and Whisker plot for target(s) plaque levels by
hypercholesterolemia (
risk614) group (no, yes) - Box and Whisker plot for target(s) plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for target(s) plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
LDL_finalCU >= 100 & LDL_finalCU <= 129 ~ "100-129",
LDL_finalCU >= 130 & LDL_finalCU <= 159 ~ "130-159",
LDL_finalCU >= 160 & LDL_finalCU <= 189 ~ "160-189",
LDL_finalCU >= 190 ~ "190+")))
table(AERNASE.clin.hdac9$LDLGroup, AERNASE.clin.hdac9$Gender)
female male
<100 45 142
100-129 25 73
130-159 18 42
160-189 9 21
190+ 2 9require(sjlabelled)
AERNASE.clin.hdac9$risk614 <- to_factor(AERNASE.clin.hdac9$risk614)
# Fix plaquephenotypes
attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"Hypercholesterolemia"] <- NA
# AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "missing value"] <- NA
# AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "no"] <- "no"
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "yes"] <- "yes"
detach(AERNASE.clin.hdac9)
table(AERNASE.clin.hdac9$risk614, AERNASE.clin.hdac9$Hypercholesterolemia)
no yes
missing value 0 0
no 191 0
yes 0 404# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)Now we can draw some graphs of plaque target(s) levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.
compare_means(HDAC9 ~ LDLGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "LDLGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypercholesterolemia, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia",
ylab = "HDAC9 (normalized expression))",
color = "Hypercholesterolemia",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Med.Statin.LLD, data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9",
xlab = "Lipid-lowering drug use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9",
xlab = "Lipid-lowering drug use per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in image
compare_means(HDAC9_max100 ~ LDLGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9_max100",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "LDLGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9_max100",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Hypercholesterolemia, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9_max100",
xlab = "Diagnosed hypercholesterolemia",
ylab = "HDAC9 (normalized expression))",
color = "Hypercholesterolemia",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypercholesterolemia.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9_max100",
xlab = "Diagnosed hypercholesterolemia per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Med.Statin.LLD, data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9_max100",
xlab = "Lipid-lowering drug use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Med.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9_max100",
xlab = "Lipid-lowering drug use per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "HDAC9_max100",
xlab = "LDL (mg/dL) per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)),
x = c("Hypercholesterolemia"),
y = "HDAC9_max100",
xlab = "Diagnosed hypercholesterolemia per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in imageKidney function (eGFR)
We want to create figures of target(s) levels stratified by kidney function.
- Box and Whisker plot for target(s) plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for target(s) plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
GFR_MDRD >= 15 & GFR_MDRD <= 29 ~ "15-29",
GFR_MDRD >= 30 & GFR_MDRD <= 59 ~ "30-59",
GFR_MDRD >= 60 & GFR_MDRD <= 89 ~ "60-89",
GFR_MDRD >= 90 ~ "90+")))
table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$Gender)
female male
15-29 2 6
30-59 38 101
60-89 84 249
90+ 25 86table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$KDOQI)
No data available/missing Normal kidney function CKD 2 (Mild) CKD 3 (Moderate) CKD 4 (Severe) CKD 5 (Failure)
15-29 0 0 0 0 8 0
30-59 0 0 0 139 0 0
60-89 0 0 333 0 0 0
90+ 0 111 0 0 0 0Now we can draw some graphs of plaque target(s) levels per sex and kidney function group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2)",
ylab = "HDAC9 (normalized expression)",
color = "eGFRGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ eGFRGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ KDOQI, data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9",
xlab = "Kidney function (KDOQI)",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ KDOQI, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9",
xlab = "Kidney function (KDOQI) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_KDOQI.pdf"), plot = last_plot())Saving 12 x 8 in image
# Global test
compare_means(HDAC9_max100 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9_max100",
xlab = "eGFR (mL/min per 1.73 m2)",
ylab = "HDAC9 (normalized expression)",
color = "eGFRGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ eGFRGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9_max100",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ KDOQI, data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9_max100",
xlab = "Kidney function (KDOQI)",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.KDOQI.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ KDOQI, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9_max100",
xlab = "Kidney function (KDOQI) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.KDOQI_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "HDAC9_max100",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR_KDOQI.pdf"), plot = last_plot())Saving 12 x 8 in imageBMI
We want to create figures of target(s) levels stratified by BMI.
- Box and Whisker plot for target(s) plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for target(s) plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
BMI >= 18.5 & BMI < 25 ~ "18.5-24",
BMI >= 25 & BMI < 30 ~ "25-29",
BMI >= 30 & BMI < 35 ~ "30-35",
BMI >= 35 ~ "35+")))
# require(labelled)
# AERNASE.clin.hdac9$BMI_US <- as_factor(AERNASE.clin.hdac9$BMI_US)
# AERNASE.clin.hdac9$BMI_WHO <- as_factor(AERNASE.clin.hdac9$BMI_WHO)
# table(AERNASE.clin.hdac9$BMI_WHO, AERNASE.clin.hdac9$BMI_US)
table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$Gender)
female male
<18.5 3 2
18.5-24 46 162
25-29 68 220
30-35 18 55
35+ 6 12table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$BMI_WHO)
No data available/missing Underweight Normal Overweight Obese
<18.5 0 5 0 0 0
18.5-24 0 0 208 0 0
25-29 0 0 0 287 0
30-35 0 0 0 0 73
35+ 0 0 0 0 18Now we can draw some graphs of plaque MCP1 levels per sex and age group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2)",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "BMIGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "HDAC9 (normalized expression)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byWHO.pdf"), plot = last_plot())Saving 12 x 8 in image
# Global test
compare_means(HDAC9_max100 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9_max100",
xlab = "BMI groups (kg/m2)",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "BMIGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.BMI.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9_max100",
xlab = "BMI groups (kg/m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.BMI_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("BMIGroup"),
y = "HDAC9_max100",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "HDAC9 (normalized expression)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.BMI_byWHO.pdf"), plot = last_plot())Saving 12 x 8 in imageDiabetes
We want to create figures of target(s) levels stratified by type 2 diabetes.
- Box and Whisker plot for target(s) plaque levels by type 2 diabetes group (no, yes)
Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ DiabetesStatus, data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "HDAC9",
xlab = "Diabetes status",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "DiabetesStatus",
palette = "npg",
add = c("median_iqr", "jitter")) +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Diabetes.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "HDAC9",
xlab = "Diabetes status per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Diabetes_byGender.pdf"), plot = last_plot())Saving 12 x 8 in image
# Global test
compare_means(HDAC9_max100 ~ DiabetesStatus, data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "HDAC9_max100",
xlab = "Diabetes status",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "DiabetesStatus",
palette = "npg",
add = c("median_iqr", "jitter")) +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Diabetes.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "HDAC9_max100",
xlab = "Diabetes status per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Diabetes_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imageSmoking
We want to create figures of target(s) levels stratified by smoking.
- Box and Whisker plot for target(s) plaque levels by smoking group (never, ex, current)
Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ SmokerStatus, data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "HDAC9",
xlab = "Smoker status",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "SmokerStatus",
palette = "npg",
add = c("median_iqr", "jitter")) +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Smoking.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "HDAC9",
xlab = "Smoker status per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Smoking_byGender.pdf"), plot = last_plot())Saving 12 x 8 in image
# Global test
compare_means(HDAC9_max100 ~ SmokerStatus, data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "HDAC9_max100",
xlab = "Smoker status",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "SmokerStatus",
palette = "npg",
add = c("median_iqr", "jitter")) +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Smoking.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "HDAC9_max100",
xlab = "Smoker status per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Smoking_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imageStenosis
We want to create figures of target(s) levels stratified by stenosis grade.
- Box and Whisker plot for target(s) plaque levels by stenosis grade group (<70, 70-89, 90+)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
stenose == "0-49%" ~ "<70",
stenose == "50-70%" ~ "<70",
stenose == "70-90%" ~ "70-89",
stenose == "50-99%" ~ "90+",
stenose == "70-99%" ~ "90+",
stenose == "100% (Occlusion)" ~ "90+",
stenose == "90-99%" ~ "90+")))
table(AERNASE.clin.hdac9$StenoticGroup, AERNASE.clin.hdac9$Gender)
female male
<70 6 34
70-89 72 199
90+ 69 221table(AERNASE.clin.hdac9$stenose, AERNASE.clin.hdac9$StenoticGroup)
<70 70-89 90+
missing 0 0 0
0-49% 2 0 0
50-70% 38 0 0
70-90% 0 271 0
90-99% 0 0 284
100% (Occlusion) 0 0 5
NA 0 0 0
50-99% 0 0 1
70-99% 0 0 0
99 0 0 0Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ StenoticGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "HDAC9",
xlab = "Stenotic grade",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "StenoticGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Stenosis.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "HDAC9",
xlab = "Stenotic grade per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Stenosis_byGender.pdf"), plot = last_plot())Saving 12 x 8 in image
# Global test
compare_means(HDAC9_max100 ~ StenoticGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "HDAC9_max100",
xlab = "Stenotic grade",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "StenoticGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Stenosis.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "HDAC9_max100",
xlab = "Stenotic grade per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Stenosis_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imageSymptoms
We want to create per-symptom figures.
library(dplyr)
table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$AsymptSympt2G)
Asymptomatic Symptomatic
<55 10 28
55-64 24 143
65-74 30 219
75-84 15 141
85+ 1 12table(AERNASE.clin.hdac9$Gender, AERNASE.clin.hdac9$AsymptSympt2G)
Asymptomatic Symptomatic
female 15 138
male 65 405table(AERNASE.clin.hdac9$AsymptSympt2G)
Asymptomatic Symptomatic
80 543 Now we can draw some graphs of plaque target(s) levels per symptom group as median ± interquartile range.
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
compare_means(HDAC9 ~ AsymptSympt2G, data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "AsymptSympt2G", y = "HDAC9",
title = "HDAC9 (normalized expression) levels per symptom",
xlab = "Symptoms",
ylab = "HDAC9 (normalized expression)",
color = "AsymptSympt2G",
# palette = c(uithof_color[16], uithof_color[23]),
palette = "npg",
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AsymptSympt2G.pdf"), plot = last_plot())Saving 12 x 8 in imagerm(p1)compare_means(HDAC9 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "AsymptSympt2G", y = "HDAC9",
title = "HDAC9 (normalized expression) levels per symptom by gender",
xlab = "Symptoms",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagerm(p1)Alternative graph
# ?ggpubr::ggboxplot()
# compare_means(LRCH1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
# ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup))
cat("Get summary statistics for target:\n")Get summary statistics for target:summary(AERNASE.clin.hdac9$HDAC9) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 11.00 21.00 28.32 37.00 449.00 cat("\nCount number of values > 100 for target:\n")
Count number of values > 100 for target:sum(AERNASE.clin.hdac9$HDAC9 > 100)[1] 12cat("\nSetting values > 100 for target to 100.\n")
Setting values > 100 for target to 100.temp <- AERNASE.clin.hdac9 %>%
filter(!is.na(AsymptSympt2G))
temp$HDAC9[temp$HDAC9 > 100] <- 100
cat("Get summary statistics for target after fixing values:\n")Get summary statistics for target after fixing values:summary(temp$HDAC9) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 11.00 21.00 26.71 37.00 100.00 my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
compare_means(HDAC9 ~ AsymptSympt2G, data = temp, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(temp,
x = "AsymptSympt2G", y = "HDAC9",
title = "HDAC9 (normalized expression) levels per symptom",
xlab = "Symptoms",
ylab = "HDAC9 (normalized expression)",
color = "AsymptSympt2G",
# palette = c(uithof_color[16], uithof_color[23]),
palette = "npg",
add = "jitter", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.pdf"), plot = last_plot())Saving 12 x 8 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.ps"), plot = last_plot())Saving 12 x 8 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.png"), plot = last_plot())Saving 12 x 8 in imagerm(p1, temp)Forest plots
We would also like to visualize the multivariable analyses results.
library(ggplot2)
library(openxlsx)
# model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
# model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target <- read.xlsx(paste0(OUT_loc, "/20230531.AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/20230531.AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target$model <- "univariate"
model2_target$model <- "multivariate"
models_target <- rbind(model1_target, model2_target)
models_targetNAForest plots.
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_target$OR[models_target$Predictor=="HDAC9"]),
low = c(models_target$low95CI[models_target$Predictor=="HDAC9"]),
high = c(models_target$up95CI[models_target$Predictor=="HDAC9"]))
fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) +
geom_hline(yintercept = 1, lty = 2) + # add a dotted line at x=1 after flip
coord_flip(ylim = c(0.8, 1.7)) + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
ggtitle("Normalized HDAC9 expression") +
theme_minimal() # use a white background
print(fp)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.forest.pdf"), plot = fp)
rm(fp)HDAC9 vs. cytokines plaque levels correlations
We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:
- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB
In addition we will look at three metalloproteinases which were measured using an activity assay.
- MMP2
- MMP8
- MMP9
The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the target(s)` plaque levels.
We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.
Prepare data
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"
# fix names
names(AERNASE.clin.hdac9)[names(AERNASE.clin.hdac9) == "IL6"] <- "IL6rna"
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21",
"INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1",
"TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")
AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9,
subset(AEDB.CEA, select = c("STUDY_NUMBER",
cytokines,
metalloproteinases)),
by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
proteins_of_interest <- c(cytokines, metalloproteinases)
proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))
# make variables numerics()
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
mutate_each(funs(as.numeric), proteins_of_interest)Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
for(PROTEIN in 1:length(proteins_of_interest)){
# UCORBIOGSAqc$Z <- NULL
var.temp.rank = proteins_of_interest_rank[PROTEIN]
var.temp = proteins_of_interest[PROTEIN]
cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
cat(paste0("* changing ", var.temp, " to numeric.\n"))
# AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(AERNASE.clin.hdac9[,var.temp] == replace(AERNASE.clin.hdac9[,var.temp], AERNASE.clin.hdac9[,var.temp]==0, NA))
AERNASE.clin.hdac9[,var.temp][AERNASE.clin.hdac9[,var.temp]==0.000000]=NA
cat(paste0("* standardising ", var.temp,
" (mean: ",round(mean(!is.na(AERNASE.clin.hdac9[,var.temp])), digits = 6),
", n = ",sum(!is.na(AERNASE.clin.hdac9[,var.temp])),").\n"))
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
mutate_at(vars(var.temp),
# list(Z = ~ (AERNASE.clin.hdac9[,var.temp] - mean(AERNASE.clin.hdac9[,var.temp], na.rm = TRUE))/sd(AERNASE.clin.hdac9[,var.temp], na.rm = TRUE))
list(RANK = ~ qnorm((rank(AERNASE.clin.hdac9[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.hdac9[,var.temp]))))
)
# str(UCORBIOGSAqc$Z)
cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
AERNASE.clin.hdac9[,var.temp.rank] <- NULL
names(AERNASE.clin.hdac9)[names(AERNASE.clin.hdac9) == "RANK"] <- var.temp.rank
}
Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.295345, n = 184).
* renaming RANK to IL2_rank.
Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.266453, n = 166).
* renaming RANK to IL4_rank.
Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.29053, n = 181).
* renaming RANK to IL5_rank.
Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.306581, n = 191).
* renaming RANK to IL6_rank.
Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.288925, n = 180).
* renaming RANK to IL8_rank.
Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.338684, n = 211).
* renaming RANK to IL9_rank.
Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.255217, n = 159).
* renaming RANK to IL10_rank.
Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.269663, n = 168).
* renaming RANK to IL12_rank.
Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.372392, n = 232).
* renaming RANK to IL13_rank.
Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.372392, n = 232).
* renaming RANK to IL21_rank.
Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.280899, n = 175).
* renaming RANK to INFG_rank.
Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.266453, n = 166).
* renaming RANK to TNFA_rank.
Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.372392, n = 232).
* renaming RANK to MIF_rank.
Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.369181, n = 230).
* renaming RANK to MCP1_rank.
Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.345104, n = 215).
* renaming RANK to MIP1a_rank.
Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.365971, n = 228).
* renaming RANK to RANTES_rank.
Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.365971, n = 228).
* renaming RANK to MIG_rank.
Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.333868, n = 208).
* renaming RANK to IP10_rank.
Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.372392, n = 232).
* renaming RANK to Eotaxin1_rank.
Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.329053, n = 205).
* renaming RANK to TARC_rank.
Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.372392, n = 232).
* renaming RANK to PARC_rank.
Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.346709, n = 216).
* renaming RANK to MDC_rank.
Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.372392, n = 232).
* renaming RANK to OPG_rank.
Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.372392, n = 232).
* renaming RANK to sICAM1_rank.
Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.322632, n = 201).
* renaming RANK to VEGFA_rank.
Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.372392, n = 232).
* renaming RANK to TGFB_rank.
Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.373997, n = 233).
* renaming RANK to MMP2_rank.
Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.373997, n = 233).
* renaming RANK to MMP8_rank.
Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.373997, n = 233).
* renaming RANK to MMP9_rank.# rm(var.temp, var.temp.rank)Visualize transformations
We will just visualize these transformations.
proteins_of_interest_rank_target <- c("HDAC9", proteins_of_interest_rank)
proteins_of_interest_target <- c("HDAC9", proteins_of_interest)
for(PROTEIN in proteins_of_interest_target){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AERNASE.clin.hdac9, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "",
ggtheme = theme_minimal())
print(p1)
}Plotting protein HDAC9.Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.
for(PROTEIN in proteins_of_interest_rank_target){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AERNASE.clin.hdac9, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "inverse-normal transformation",
ggtheme = theme_minimal())
print(p1)
}Plotting protein HDAC9.Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.
NACorrelations
Here we calculate correlations between target(s) and 28 other cytokines. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:
- L: LUMINEX
- E: ELISA
- a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Using github PAT from envvar GITHUB_PAT
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (0a85456d) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin.hdac9,
select = c(proteins_of_interest_rank_target)
)
# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)
corr_biomarkers.rank <- round(cor(matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
rename_proteins_of_interest_target <- c("HDAC9 (RNA)",
"IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12",
"IL13 (L)", "IL21 (L)",
"INFG", "TNFA", "MIF (L)",
"MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)",
"Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)",
"OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Warning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with tiesWarning: Cannot compute exact p-value with ties# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"
DT::datatable(corr_biomarkers.rank.df)
fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
tl.cex = 16,
# xlab = c("MCP1"),
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())Saving 12 x 8 in imageggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())Saving 12 x 8 in imagerm(p1)While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of target(s)` with other cytokines only.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HDAC9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold[1] 2.763428library(dplyr)
library(tidyr)
library(ggplot2)
p1 <- ggpubr::ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = "Spearman's rho", # expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 1.25
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18)) 
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.png"), plot = last_plot())Saving 12 x 8 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.pdf"), plot = last_plot())Saving 12 x 8 in imagerm(p1)Another version - probably not good.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HDAC9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold[1] 2.763428p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
color = "CytokineY", #fill = "CytokineY", # Color by groups
palette = uithof_color, # Custom color palette
xlab = "Cytokine",
ylab = "log10(P)", # expression(log[10]~"("~italic(p)~")-value"),
# ylim = c(0, 9),
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = FALSE, # Rotate vertically
# group = "CytokineY", # Order by groups
dot.size = 16, # Large dot size
label = round(temp$SpearmanRho, digits = 3), # Add mpg values as dot labels
font.label = list(color = "white", size = 12,
vjust = 0.5)
)
ggpar(p1, legend = "",
legend.title = "") +
theme(axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18))
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.dotchart.png"), plot = last_plot())Saving 12 x 8 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.dotchart.pdf"), plot = last_plot())Saving 12 x 8 in imagerm(temp, p1)HDAC9 vs. cytokines plaque levels
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines traits as a function of plaque target(s) levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.TARGET.RANK)) {
PROTEIN = TRAITS.TARGET.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
# fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of HDAC9.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
25.54
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-27.394 -14.573 -4.190 8.337 162.308
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 87.089523 67.253755 1.295 0.197
currentDF[, TRAIT] -2.065605 1.865974 -1.107 0.270
Age 0.069517 0.225799 0.308 0.759
Gendermale 2.076675 4.380429 0.474 0.636
ORdate_epoch -0.005455 0.005311 -1.027 0.306
Residual standard error: 24.79 on 179 degrees of freedom
Multiple R-squared: 0.01339, Adjusted R-squared: -0.008654
F-statistic: 0.6075 on 4 and 179 DF, p-value: 0.6577
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL2_rank
Effect size...............: -2.065605
Standard error............: 1.865974
Odds ratio (effect size)..: 0.127
Lower 95% CI..............: 0.003
Upper 95% CI..............: 4.912
T-value...................: -1.106984
P-value...................: 0.269786
R^2.......................: 0.013393
Adjusted r^2..............: -0.008654
Sample size of AE DB......: 623
Sample size of model......: 184
Missing data %............: 70.46549
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
26.58
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-28.084 -17.297 -5.344 5.559 218.337
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 60.651036 91.603633 0.662 0.509
currentDF[, TRAIT] -0.561106 2.475638 -0.227 0.821
Age 0.131814 0.297249 0.443 0.658
Gendermale 3.948413 5.833017 0.677 0.499
ORdate_epoch -0.003716 0.007257 -0.512 0.609
Residual standard error: 30.89 on 161 degrees of freedom
Multiple R-squared: 0.006257, Adjusted R-squared: -0.01843
F-statistic: 0.2534 on 4 and 161 DF, p-value: 0.9072
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL4_rank
Effect size...............: -0.561106
Standard error............: 2.475638
Odds ratio (effect size)..: 0.571
Lower 95% CI..............: 0.004
Upper 95% CI..............: 73.05
T-value...................: -0.226651
P-value...................: 0.8209826
R^2.......................: 0.006257
Adjusted r^2..............: -0.018433
Sample size of AE DB......: 623
Sample size of model......: 166
Missing data %............: 73.35474
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
26.19
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-27.186 -16.269 -5.412 4.875 219.405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.721510 83.734923 0.880 0.380
currentDF[, TRAIT] -1.776930 2.284343 -0.778 0.438
Age 0.093037 0.276642 0.336 0.737
Gendermale 3.730928 5.381239 0.693 0.489
ORdate_epoch -0.004567 0.006698 -0.682 0.496
Residual standard error: 29.97 on 176 degrees of freedom
Multiple R-squared: 0.009617, Adjusted R-squared: -0.01289
F-statistic: 0.4273 on 4 and 176 DF, p-value: 0.7888
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL5_rank
Effect size...............: -1.77693
Standard error............: 2.284343
Odds ratio (effect size)..: 0.169
Lower 95% CI..............: 0.002
Upper 95% CI..............: 14.885
T-value...................: -0.777873
P-value...................: 0.4376881
R^2.......................: 0.009617
Adjusted r^2..............: -0.012891
Sample size of AE DB......: 623
Sample size of model......: 181
Missing data %............: 70.94703
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
26.04
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-28.063 -16.072 -5.804 5.847 220.416
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.746906 73.136217 0.680 0.497
currentDF[, TRAIT] -1.772356 2.180601 -0.813 0.417
Age 0.123328 0.264826 0.466 0.642
Gendermale 2.105297 4.940344 0.426 0.670
ORdate_epoch -0.002699 0.005824 -0.464 0.644
Residual standard error: 29.35 on 186 degrees of freedom
Multiple R-squared: 0.008199, Adjusted R-squared: -0.01313
F-statistic: 0.3844 on 4 and 186 DF, p-value: 0.8196
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL6_rank
Effect size...............: -1.772356
Standard error............: 2.180601
Odds ratio (effect size)..: 0.17
Lower 95% CI..............: 0.002
Upper 95% CI..............: 12.202
T-value...................: -0.812783
P-value...................: 0.4173817
R^2.......................: 0.008199
Adjusted r^2..............: -0.01313
Sample size of AE DB......: 623
Sample size of model......: 191
Missing data %............: 69.34189
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
25.21
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-26.603 -15.456 -4.631 5.804 221.136
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 67.269066 72.441025 0.929 0.354
currentDF[, TRAIT] 1.086280 2.162178 0.502 0.616
Age 0.087404 0.247409 0.353 0.724
Gendermale -0.027405 4.923287 -0.006 0.996
ORdate_epoch -0.003844 0.005758 -0.668 0.505
Residual standard error: 27.72 on 175 degrees of freedom
Multiple R-squared: 0.00367, Adjusted R-squared: -0.0191
F-statistic: 0.1611 on 4 and 175 DF, p-value: 0.9577
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL8_rank
Effect size...............: 1.08628
Standard error............: 2.162178
Odds ratio (effect size)..: 2.963
Lower 95% CI..............: 0.043
Upper 95% CI..............: 205.234
T-value...................: 0.502401
P-value...................: 0.6160173
R^2.......................: 0.00367
Adjusted r^2..............: -0.019104
Sample size of AE DB......: 623
Sample size of model......: 180
Missing data %............: 71.10754
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
28.024 4.624
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-41.38 -17.94 -8.00 5.53 414.61
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -73.867362 92.194148 -0.801 0.4239
currentDF[, TRAIT] 4.940574 2.842073 1.738 0.0836 .
Age 0.298577 0.351069 0.850 0.3960
Gendermale 5.353704 6.545811 0.818 0.4144
ORdate_epoch 0.006181 0.007047 0.877 0.3814
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 40.88 on 206 degrees of freedom
Multiple R-squared: 0.0226, Adjusted R-squared: 0.003626
F-statistic: 1.191 on 4 and 206 DF, p-value: 0.3158
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL9_rank
Effect size...............: 4.940574
Standard error............: 2.842073
Odds ratio (effect size)..: 139.851
Lower 95% CI..............: 0.533
Upper 95% CI..............: 36718.55
T-value...................: 1.73837
P-value...................: 0.0836398
R^2.......................: 0.022604
Adjusted r^2..............: 0.003626
Sample size of AE DB......: 623
Sample size of model......: 211
Missing data %............: 66.13162
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
24.83
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-28.342 -15.946 -5.041 5.721 165.217
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 124.909366 82.062646 1.522 0.130
currentDF[, TRAIT] -0.587872 2.155866 -0.273 0.785
Age 0.084426 0.257199 0.328 0.743
Gendermale 2.419501 4.987664 0.485 0.628
ORdate_epoch -0.008691 0.006431 -1.352 0.178
Residual standard error: 26.07 on 154 degrees of freedom
Multiple R-squared: 0.01389, Adjusted R-squared: -0.01172
F-statistic: 0.5422 on 4 and 154 DF, p-value: 0.7049
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL10_rank
Effect size...............: -0.587872
Standard error............: 2.155866
Odds ratio (effect size)..: 0.556
Lower 95% CI..............: 0.008
Upper 95% CI..............: 38.001
T-value...................: -0.272685
P-value...................: 0.7854607
R^2.......................: 0.013889
Adjusted r^2..............: -0.011725
Sample size of AE DB......: 623
Sample size of model......: 159
Missing data %............: 74.47833
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.23
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-28.668 -16.162 -5.598 6.304 215.169
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 74.693706 91.325457 0.818 0.415
currentDF[, TRAIT] -2.873685 2.439555 -1.178 0.241
Age 0.087055 0.296200 0.294 0.769
Gendermale 4.447111 5.708970 0.779 0.437
ORdate_epoch -0.004576 0.007217 -0.634 0.527
Residual standard error: 30.69 on 163 degrees of freedom
Multiple R-squared: 0.01495, Adjusted R-squared: -0.00922
F-statistic: 0.6186 on 4 and 163 DF, p-value: 0.6499
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL12_rank
Effect size...............: -2.873685
Standard error............: 2.439555
Odds ratio (effect size)..: 0.056
Lower 95% CI..............: 0
Upper 95% CI..............: 6.739
T-value...................: -1.177955
P-value...................: 0.2405317
R^2.......................: 0.014953
Adjusted r^2..............: -0.00922
Sample size of AE DB......: 623
Sample size of model......: 168
Missing data %............: 73.03371
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-35.36 -17.66 -7.84 5.55 415.08
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -64.323527 87.644761 -0.734 0.464
currentDF[, TRAIT] 3.132263 2.637430 1.188 0.236
Age 0.308054 0.321748 0.957 0.339
Gendermale 4.216680 6.052396 0.697 0.487
ORdate_epoch 0.005405 0.006755 0.800 0.424
Residual standard error: 39.53 on 227 degrees of freedom
Multiple R-squared: 0.01487, Adjusted R-squared: -0.00249
F-statistic: 0.8566 on 4 and 227 DF, p-value: 0.4908
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL13_rank
Effect size...............: 3.132263
Standard error............: 2.63743
Odds ratio (effect size)..: 22.926
Lower 95% CI..............: 0.13
Upper 95% CI..............: 4030.42
T-value...................: 1.18762
P-value...................: 0.2362245
R^2.......................: 0.014869
Adjusted r^2..............: -0.00249
Sample size of AE DB......: 623
Sample size of model......: 232
Missing data %............: 62.76084
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-31.99 -17.35 -7.93 5.69 415.55
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -67.795115 87.695514 -0.773 0.440
currentDF[, TRAIT] 2.145421 2.630724 0.816 0.416
Age 0.285867 0.321271 0.890 0.375
Gendermale 4.278613 6.068842 0.705 0.482
ORdate_epoch 0.005798 0.006750 0.859 0.391
Residual standard error: 39.59 on 227 degrees of freedom
Multiple R-squared: 0.01164, Adjusted R-squared: -0.005772
F-statistic: 0.6686 on 4 and 227 DF, p-value: 0.6144
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL21_rank
Effect size...............: 2.145421
Standard error............: 2.630724
Odds ratio (effect size)..: 8.546
Lower 95% CI..............: 0.049
Upper 95% CI..............: 1482.729
T-value...................: 0.815525
P-value...................: 0.4156266
R^2.......................: 0.011644
Adjusted r^2..............: -0.005772
Sample size of AE DB......: 623
Sample size of model......: 232
Missing data %............: 62.76084
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.45
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-27.768 -16.019 -5.336 5.937 217.710
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.200209 87.073464 0.554 0.581
currentDF[, TRAIT] -1.199789 2.508853 -0.478 0.633
Age 0.107104 0.287959 0.372 0.710
Gendermale 2.281405 5.736982 0.398 0.691
ORdate_epoch -0.002397 0.006842 -0.350 0.727
Residual standard error: 30.55 on 170 degrees of freedom
Multiple R-squared: 0.003914, Adjusted R-squared: -0.01952
F-statistic: 0.167 on 4 and 170 DF, p-value: 0.9549
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: INFG_rank
Effect size...............: -1.199789
Standard error............: 2.508853
Odds ratio (effect size)..: 0.301
Lower 95% CI..............: 0.002
Upper 95% CI..............: 41.164
T-value...................: -0.478222
P-value...................: 0.6331066
R^2.......................: 0.003914
Adjusted r^2..............: -0.019523
Sample size of AE DB......: 623
Sample size of model......: 175
Missing data %............: 71.91011
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
26.81
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-27.719 -16.423 -5.891 5.925 215.388
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 70.099936 90.904819 0.771 0.442
currentDF[, TRAIT] -2.394261 2.429518 -0.985 0.326
Age 0.167410 0.298046 0.562 0.575
Gendermale 3.082801 5.659187 0.545 0.587
ORdate_epoch -0.004591 0.007154 -0.642 0.522
Residual standard error: 30.81 on 161 degrees of freedom
Multiple R-squared: 0.01304, Adjusted R-squared: -0.01148
F-statistic: 0.5317 on 4 and 161 DF, p-value: 0.7126
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: TNFA_rank
Effect size...............: -2.394261
Standard error............: 2.429518
Odds ratio (effect size)..: 0.091
Lower 95% CI..............: 0.001
Upper 95% CI..............: 10.672
T-value...................: -0.985488
P-value...................: 0.3258631
R^2.......................: 0.013037
Adjusted r^2..............: -0.011484
Sample size of AE DB......: 623
Sample size of model......: 166
Missing data %............: 73.35474
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-30.82 -17.23 -7.33 4.97 414.47
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -83.502796 97.752759 -0.854 0.394
currentDF[, TRAIT] 0.862190 2.915813 0.296 0.768
Age 0.265892 0.320651 0.829 0.408
Gendermale 4.696186 6.059564 0.775 0.439
ORdate_epoch 0.007132 0.007526 0.948 0.344
Residual standard error: 39.64 on 227 degrees of freedom
Multiple R-squared: 0.00913, Adjusted R-squared: -0.00833
F-statistic: 0.5229 on 4 and 227 DF, p-value: 0.719
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MIF_rank
Effect size...............: 0.86219
Standard error............: 2.915813
Odds ratio (effect size)..: 2.368
Lower 95% CI..............: 0.008
Upper 95% CI..............: 718.513
T-value...................: 0.295695
P-value...................: 0.7677336
R^2.......................: 0.00913
Adjusted r^2..............: -0.00833
Sample size of AE DB......: 623
Sample size of model......: 232
Missing data %............: 62.76084
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-31.89 -17.58 -7.68 5.16 414.82
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -74.500811 90.064634 -0.827 0.409
currentDF[, TRAIT] 0.588396 2.698328 0.218 0.828
Age 0.283143 0.325825 0.869 0.386
Gendermale 4.660364 6.137923 0.759 0.448
ORdate_epoch 0.006329 0.006905 0.917 0.360
Residual standard error: 39.81 on 225 degrees of freedom
Multiple R-squared: 0.009151, Adjusted R-squared: -0.008464
F-statistic: 0.5195 on 4 and 225 DF, p-value: 0.7215
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MCP1_rank
Effect size...............: 0.588396
Standard error............: 2.698328
Odds ratio (effect size)..: 1.801
Lower 95% CI..............: 0.009
Upper 95% CI..............: 356.779
T-value...................: 0.218059
P-value...................: 0.8275805
R^2.......................: 0.009151
Adjusted r^2..............: -0.008464
Sample size of AE DB......: 623
Sample size of model......: 230
Missing data %............: 63.08186
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.93
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-31.49 -17.37 -8.05 5.80 415.36
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -64.714720 91.395195 -0.708 0.480
currentDF[, TRAIT] 1.715264 2.798550 0.613 0.541
Age 0.274716 0.340846 0.806 0.421
Gendermale 5.269177 6.525565 0.807 0.420
ORdate_epoch 0.005579 0.007011 0.796 0.427
Residual standard error: 40.79 on 210 degrees of freedom
Multiple R-squared: 0.01076, Adjusted R-squared: -0.008082
F-statistic: 0.5711 on 4 and 210 DF, p-value: 0.6839
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MIP1a_rank
Effect size...............: 1.715264
Standard error............: 2.79855
Odds ratio (effect size)..: 5.558
Lower 95% CI..............: 0.023
Upper 95% CI..............: 1339.997
T-value...................: 0.612912
P-value...................: 0.540598
R^2.......................: 0.010761
Adjusted r^2..............: -0.008082
Sample size of AE DB......: 623
Sample size of model......: 215
Missing data %............: 65.48957
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_epoch,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_epoch
-123.25756 5.92451 0.01202
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-35.67 -18.04 -6.88 6.44 395.66
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.687e+02 9.755e+01 -1.729 0.0851 .
currentDF[, TRAIT] 6.668e+00 2.933e+00 2.274 0.0239 *
Age 4.110e-01 3.267e-01 1.258 0.2096
Gendermale 4.950e+00 6.135e+00 0.807 0.4206
ORdate_epoch 1.311e-02 7.392e-03 1.774 0.0774 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 39.53 on 223 degrees of freedom
Multiple R-squared: 0.03151, Adjusted R-squared: 0.01414
F-statistic: 1.814 on 4 and 223 DF, p-value: 0.1271
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: RANTES_rank
Effect size...............: 6.667753
Standard error............: 2.932656
Odds ratio (effect size)..: 786.626
Lower 95% CI..............: 2.509
Upper 95% CI..............: 246658.3
T-value...................: 2.273622
P-value...................: 0.0239406
R^2.......................: 0.031508
Adjusted r^2..............: 0.014136
Sample size of AE DB......: 623
Sample size of model......: 228
Missing data %............: 63.40289
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.69
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-35.99 -17.26 -8.55 5.93 414.90
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -40.985877 91.349071 -0.449 0.654
currentDF[, TRAIT] 3.612208 2.811494 1.285 0.200
Age 0.342073 0.326664 1.047 0.296
Gendermale 4.512213 6.158990 0.733 0.465
ORdate_epoch 0.003351 0.007118 0.471 0.638
Residual standard error: 39.8 on 223 degrees of freedom
Multiple R-squared: 0.01671, Adjusted R-squared: -0.0009268
F-statistic: 0.9475 on 4 and 223 DF, p-value: 0.4373
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MIG_rank
Effect size...............: 3.612208
Standard error............: 2.811494
Odds ratio (effect size)..: 37.048
Lower 95% CI..............: 0.15
Upper 95% CI..............: 9161.234
T-value...................: 1.2848
P-value...................: 0.200195
R^2.......................: 0.016711
Adjusted r^2..............: -0.000927
Sample size of AE DB......: 623
Sample size of model......: 228
Missing data %............: 63.40289
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
28.07
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-38.27 -17.56 -6.96 4.73 415.23
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -88.691124 94.061954 -0.943 0.347
currentDF[, TRAIT] 4.227637 2.907662 1.454 0.148
Age 0.379027 0.355347 1.067 0.287
Gendermale 6.676987 6.485221 1.030 0.304
ORdate_epoch 0.006867 0.007196 0.954 0.341
Residual standard error: 41.12 on 203 degrees of freedom
Multiple R-squared: 0.02242, Adjusted R-squared: 0.003156
F-statistic: 1.164 on 4 and 203 DF, p-value: 0.3279
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IP10_rank
Effect size...............: 4.227637
Standard error............: 2.907662
Odds ratio (effect size)..: 68.555
Lower 95% CI..............: 0.23
Upper 95% CI..............: 20468.74
T-value...................: 1.453965
P-value...................: 0.1475004
R^2.......................: 0.022418
Adjusted r^2..............: 0.003156
Sample size of AE DB......: 623
Sample size of model......: 208
Missing data %............: 66.61316
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-30.85 -17.40 -8.06 4.77 415.06
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -67.733793 88.636854 -0.764 0.446
currentDF[, TRAIT] 0.637212 2.656733 0.240 0.811
Age 0.265744 0.320851 0.828 0.408
Gendermale 4.525922 6.084902 0.744 0.458
ORdate_epoch 0.005887 0.006831 0.862 0.390
Residual standard error: 39.65 on 227 degrees of freedom
Multiple R-squared: 0.008999, Adjusted R-squared: -0.008463
F-statistic: 0.5154 on 4 and 227 DF, p-value: 0.7245
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.637212
Standard error............: 2.656733
Odds ratio (effect size)..: 1.891
Lower 95% CI..............: 0.01
Upper 95% CI..............: 345.298
T-value...................: 0.239848
P-value...................: 0.8106645
R^2.......................: 0.008999
Adjusted r^2..............: -0.008463
Sample size of AE DB......: 623
Sample size of model......: 232
Missing data %............: 62.76084
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_epoch
-207.95042 6.38764 0.58156 10.11123 0.01498
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-43.88 -18.26 -7.27 6.04 405.52
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.080e+02 1.153e+02 -1.803 0.0729 .
currentDF[, TRAIT] 6.388e+00 3.113e+00 2.052 0.0415 *
Age 5.816e-01 3.580e-01 1.624 0.1059
Gendermale 1.011e+01 6.731e+00 1.502 0.1346
ORdate_epoch 1.498e-02 8.688e-03 1.724 0.0862 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.02 on 200 degrees of freedom
Multiple R-squared: 0.03995, Adjusted R-squared: 0.02075
F-statistic: 2.081 on 4 and 200 DF, p-value: 0.0847
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: TARC_rank
Effect size...............: 6.387643
Standard error............: 3.112859
Odds ratio (effect size)..: 594.454
Lower 95% CI..............: 1.332
Upper 95% CI..............: 265361
T-value...................: 2.052018
P-value...................: 0.04147065
R^2.......................: 0.039951
Adjusted r^2..............: 0.02075
Sample size of AE DB......: 623
Sample size of model......: 205
Missing data %............: 67.0947
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-35.42 -18.48 -6.53 4.32 414.90
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.131e+02 9.298e+01 -1.217 0.225
currentDF[, TRAIT] 3.700e+00 2.769e+00 1.336 0.183
Age 2.912e-01 3.198e-01 0.910 0.364
Gendermale 5.358e+00 6.058e+00 0.884 0.377
ORdate_epoch 9.316e-03 7.127e-03 1.307 0.192
Residual standard error: 39.5 on 227 degrees of freedom
Multiple R-squared: 0.01648, Adjusted R-squared: -0.000846
F-statistic: 0.9512 on 4 and 227 DF, p-value: 0.4352
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: PARC_rank
Effect size...............: 3.700443
Standard error............: 2.769259
Odds ratio (effect size)..: 40.465
Lower 95% CI..............: 0.178
Upper 95% CI..............: 9211.343
T-value...................: 1.336257
P-value...................: 0.1828032
R^2.......................: 0.016485
Adjusted r^2..............: -0.000846
Sample size of AE DB......: 623
Sample size of model......: 232
Missing data %............: 62.76084
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
28.08
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-32.41 -18.45 -7.98 5.56 414.21
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -89.676560 96.537106 -0.929 0.354
currentDF[, TRAIT] 1.866291 2.944837 0.634 0.527
Age 0.295105 0.341712 0.864 0.389
Gendermale 6.407479 6.464388 0.991 0.323
ORdate_epoch 0.007399 0.007388 1.001 0.318
Residual standard error: 40.78 on 211 degrees of freedom
Multiple R-squared: 0.01235, Adjusted R-squared: -0.006375
F-statistic: 0.6595 on 4 and 211 DF, p-value: 0.6208
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MDC_rank
Effect size...............: 1.866291
Standard error............: 2.944837
Odds ratio (effect size)..: 6.464
Lower 95% CI..............: 0.02
Upper 95% CI..............: 2075.945
T-value...................: 0.63375
P-value...................: 0.5269302
R^2.......................: 0.012348
Adjusted r^2..............: -0.006375
Sample size of AE DB......: 623
Sample size of model......: 216
Missing data %............: 65.32905
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-36.57 -18.10 -7.00 5.11 409.72
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -68.605300 87.417842 -0.785 0.433
currentDF[, TRAIT] 3.521328 2.625900 1.341 0.181
Age 0.314745 0.321536 0.979 0.329
Gendermale 4.305097 6.041532 0.713 0.477
ORdate_epoch 0.005705 0.006728 0.848 0.397
Residual standard error: 39.5 on 227 degrees of freedom
Multiple R-squared: 0.01654, Adjusted R-squared: -0.0007905
F-statistic: 0.9544 on 4 and 227 DF, p-value: 0.4334
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: OPG_rank
Effect size...............: 3.521328
Standard error............: 2.6259
Odds ratio (effect size)..: 33.829
Lower 95% CI..............: 0.197
Upper 95% CI..............: 5814.395
T-value...................: 1.340999
P-value...................: 0.1812611
R^2.......................: 0.016539
Adjusted r^2..............: -0.00079
Sample size of AE DB......: 623
Sample size of model......: 232
Missing data %............: 62.76084
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
27.62
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-29.48 -18.25 -8.39 5.43 413.01
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -56.874414 90.268329 -0.630 0.529
currentDF[, TRAIT] -1.741154 2.698161 -0.645 0.519
Age 0.229442 0.323729 0.709 0.479
Gendermale 4.463217 6.061762 0.736 0.462
ORdate_epoch 0.005222 0.006891 0.758 0.449
Residual standard error: 39.62 on 227 degrees of freedom
Multiple R-squared: 0.01056, Adjusted R-squared: -0.006872
F-statistic: 0.6059 on 4 and 227 DF, p-value: 0.6588
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: sICAM1_rank
Effect size...............: -1.741154
Standard error............: 2.698161
Odds ratio (effect size)..: 0.175
Lower 95% CI..............: 0.001
Upper 95% CI..............: 34.717
T-value...................: -0.645311
P-value...................: 0.5193772
R^2.......................: 0.010563
Adjusted r^2..............: -0.006872
Sample size of AE DB......: 623
Sample size of model......: 232
Missing data %............: 62.76084
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
26.64
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-28.190 -16.572 -6.075 5.838 216.764
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36.971813 78.220186 0.473 0.637
currentDF[, TRAIT] 1.322838 2.314499 0.572 0.568
Age 0.286058 0.256517 1.115 0.266
Gendermale 1.950015 4.785744 0.407 0.684
ORdate_epoch -0.002481 0.006148 -0.404 0.687
Residual standard error: 29.39 on 196 degrees of freedom
Multiple R-squared: 0.008052, Adjusted R-squared: -0.01219
F-statistic: 0.3977 on 4 and 196 DF, p-value: 0.8101
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: VEGFA_rank
Effect size...............: 1.322838
Standard error............: 2.314499
Odds ratio (effect size)..: 3.754
Lower 95% CI..............: 0.04
Upper 95% CI..............: 350.464
T-value...................: 0.571544
P-value...................: 0.5682857
R^2.......................: 0.008052
Adjusted r^2..............: -0.012192
Sample size of AE DB......: 623
Sample size of model......: 201
Missing data %............: 67.73676
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
28.02
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-30.46 -17.82 -8.26 6.26 413.09
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -72.077478 88.729581 -0.812 0.417
currentDF[, TRAIT] 1.362029 2.668677 0.510 0.610
Age 0.304434 0.321734 0.946 0.345
Gendermale 3.619179 5.995656 0.604 0.547
ORdate_epoch 0.006116 0.006843 0.894 0.372
Residual standard error: 39.69 on 227 degrees of freedom
Multiple R-squared: 0.009079, Adjusted R-squared: -0.008382
F-statistic: 0.5199 on 4 and 227 DF, p-value: 0.7212
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: TGFB_rank
Effect size...............: 1.362029
Standard error............: 2.668677
Odds ratio (effect size)..: 3.904
Lower 95% CI..............: 0.021
Upper 95% CI..............: 729.701
T-value...................: 0.510376
P-value...................: 0.6102839
R^2.......................: 0.009079
Adjusted r^2..............: -0.008382
Sample size of AE DB......: 623
Sample size of model......: 232
Missing data %............: 62.76084
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
25.63
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-27.299 -16.616 -4.536 6.204 220.831
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 64.470511 62.856489 1.026 0.306
currentDF[, TRAIT] -1.982845 1.871456 -1.060 0.290
Age 0.092582 0.223660 0.414 0.679
Gendermale 1.002118 4.231221 0.237 0.813
ORdate_epoch -0.003663 0.004885 -0.750 0.454
Residual standard error: 27.73 on 228 degrees of freedom
Multiple R-squared: 0.008214, Adjusted R-squared: -0.009185
F-statistic: 0.4721 on 4 and 228 DF, p-value: 0.7562
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MMP2_rank
Effect size...............: -1.982845
Standard error............: 1.871456
Odds ratio (effect size)..: 0.138
Lower 95% CI..............: 0.004
Upper 95% CI..............: 5.394
T-value...................: -1.05952
P-value...................: 0.2904841
R^2.......................: 0.008214
Adjusted r^2..............: -0.009185
Sample size of AE DB......: 623
Sample size of model......: 233
Missing data %............: 62.60032
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
25.631 2.588
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-29.511 -16.059 -4.482 5.497 221.011
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.875294 61.810526 0.888 0.376
currentDF[, TRAIT] 2.581427 1.830146 1.411 0.160
Age 0.120985 0.222350 0.544 0.587
Gendermale 1.054754 4.196582 0.251 0.802
ORdate_epoch -0.003054 0.004824 -0.633 0.527
Residual standard error: 27.68 on 228 degrees of freedom
Multiple R-squared: 0.01195, Adjusted R-squared: -0.005381
F-statistic: 0.6896 on 4 and 228 DF, p-value: 0.5999
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MMP8_rank
Effect size...............: 2.581427
Standard error............: 1.830146
Odds ratio (effect size)..: 13.216
Lower 95% CI..............: 0.366
Upper 95% CI..............: 477.475
T-value...................: 1.410503
P-value...................: 0.159754
R^2.......................: 0.011953
Adjusted r^2..............: -0.005381
Sample size of AE DB......: 623
Sample size of model......: 233
Missing data %............: 62.60032
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
25.631 2.675
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-29.540 -16.010 -5.057 5.432 219.633
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 44.113657 62.089220 0.710 0.478
currentDF[, TRAIT] 2.632786 1.827045 1.441 0.151
Age 0.122384 0.222328 0.550 0.583
Gendermale 1.787112 4.171002 0.428 0.669
ORdate_epoch -0.002246 0.004844 -0.464 0.643
Residual standard error: 27.67 on 228 degrees of freedom
Multiple R-squared: 0.01233, Adjusted R-squared: -0.005001
F-statistic: 0.7114 on 4 and 228 DF, p-value: 0.5849
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MMP9_rank
Effect size...............: 2.632786
Standard error............: 1.827045
Odds ratio (effect size)..: 13.912
Lower 95% CI..............: 0.387
Upper 95% CI..............: 499.593
T-value...................: 1.441008
P-value...................: 0.1509543
R^2.......................: 0.012326
Adjusted r^2..............: -0.005001
Sample size of AE DB......: 623
Sample size of model......: 233
Missing data %............: 62.60032 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines as a function of plaque target(s) levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.TARGET.RANK)) {
PROTEIN = TRAITS.TARGET.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
# fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
# Hypertension.composite + DiabetesStatus + SmokerStatus +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# MedHx_CVD + stenose,
# data = currentDF)
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of HDAC9.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes
27.239 -8.647
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-32.169 -14.980 -3.248 8.376 151.803
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 129.968938 82.983078 1.566 0.1194
currentDF[, TRAIT] -3.630311 2.139259 -1.697 0.0918 .
Age -0.216849 0.286416 -0.757 0.4502
Gendermale 0.869385 5.154359 0.169 0.8663
ORdate_epoch -0.005699 0.005899 -0.966 0.3356
Hypertension.compositeyes 5.520698 6.724909 0.821 0.4130
DiabetesStatusDiabetes -10.029463 5.630936 -1.781 0.0769 .
SmokerStatusEx-smoker 4.024371 4.829086 0.833 0.4060
SmokerStatusNever smoked -2.370524 6.470277 -0.366 0.7146
Med.Statin.LLDyes -6.615329 4.695073 -1.409 0.1609
Med.all.antiplateletyes -3.076332 7.510344 -0.410 0.6827
GFR_MDRD -0.069496 0.122784 -0.566 0.5722
BMI -0.540142 0.635367 -0.850 0.3966
MedHx_CVDNo 1.185006 4.475491 0.265 0.7915
stenose70-90% 3.765984 19.200092 0.196 0.8448
stenose90-99% 1.901542 19.087538 0.100 0.9208
stenose100% (Occlusion) -23.876986 27.789248 -0.859 0.3916
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 25.63 on 148 degrees of freedom
Multiple R-squared: 0.06958, Adjusted R-squared: -0.031
F-statistic: 0.6918 on 16 and 148 DF, p-value: 0.7988
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL2_rank
Effect size...............: -3.630311
Standard error............: 2.139259
Odds ratio (effect size)..: 0.027
Lower 95% CI..............: 0
Upper 95% CI..............: 1.755
T-value...................: -1.696995
P-value...................: 0.09179966
R^2.......................: 0.069581
Adjusted r^2..............: -0.031005
Sample size of AE DB......: 623
Sample size of model......: 165
Missing data %............: 73.51525
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
47.7846 -13.4243 -0.2586
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-32.858 -17.385 -4.113 5.693 201.916
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.768e+01 1.144e+02 0.854 0.3949
currentDF[, TRAIT] -8.615e-01 2.942e+00 -0.293 0.7701
Age -2.347e-01 3.866e-01 -0.607 0.5448
Gendermale 5.040e+00 6.905e+00 0.730 0.4668
ORdate_epoch -8.409e-04 8.188e-03 -0.103 0.9184
Hypertension.compositeyes 7.289e+00 9.096e+00 0.801 0.4244
DiabetesStatusDiabetes -1.427e+01 7.554e+00 -1.888 0.0612 .
SmokerStatusEx-smoker -5.237e-01 6.313e+00 -0.083 0.9340
SmokerStatusNever smoked -5.638e+00 8.802e+00 -0.641 0.5229
Med.Statin.LLDyes -6.647e+00 6.569e+00 -1.012 0.3135
Med.all.antiplateletyes -3.736e+00 9.932e+00 -0.376 0.7074
GFR_MDRD -2.673e-01 1.791e-01 -1.492 0.1381
BMI -9.417e-01 8.967e-01 -1.050 0.2956
MedHx_CVDNo 6.568e+00 6.007e+00 1.094 0.2762
stenose70-90% 5.456e-01 2.450e+01 0.022 0.9823
stenose90-99% -1.560e+00 2.432e+01 -0.064 0.9489
stenose100% (Occlusion) -3.036e+01 3.552e+01 -0.855 0.3942
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 32.3 on 130 degrees of freedom
Multiple R-squared: 0.07954, Adjusted R-squared: -0.03375
F-statistic: 0.7021 on 16 and 130 DF, p-value: 0.7877
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL4_rank
Effect size...............: -0.861483
Standard error............: 2.942255
Odds ratio (effect size)..: 0.423
Lower 95% CI..............: 0.001
Upper 95% CI..............: 135.009
T-value...................: -0.292797
P-value...................: 0.7701444
R^2.......................: 0.079542
Adjusted r^2..............: -0.033746
Sample size of AE DB......: 623
Sample size of model......: 147
Missing data %............: 76.40449
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
26.92
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-37.693 -16.052 -4.689 5.941 204.877
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 117.047668 104.499037 1.120 0.265
currentDF[, TRAIT] -3.245108 2.655469 -1.222 0.224
Age -0.138915 0.368401 -0.377 0.707
Gendermale 5.258030 6.431434 0.818 0.415
ORdate_epoch -0.004975 0.007600 -0.655 0.514
Hypertension.compositeyes 6.221788 8.538623 0.729 0.467
DiabetesStatusDiabetes -10.444662 6.934794 -1.506 0.134
SmokerStatusEx-smoker -1.924986 5.869640 -0.328 0.743
SmokerStatusNever smoked -8.234699 8.322618 -0.989 0.324
Med.Statin.LLDyes -3.648396 5.989365 -0.609 0.543
Med.all.antiplateletyes -1.858734 9.344854 -0.199 0.843
GFR_MDRD -0.139730 0.159391 -0.877 0.382
BMI -0.710326 0.789321 -0.900 0.370
MedHx_CVDNo 7.238149 5.675679 1.275 0.204
stenose70-90% 7.280024 19.713996 0.369 0.712
stenose90-99% 6.070692 19.481112 0.312 0.756
stenose100% (Occlusion) -22.734645 31.469828 -0.722 0.471
Residual standard error: 31.55 on 143 degrees of freedom
Multiple R-squared: 0.0636, Adjusted R-squared: -0.04117
F-statistic: 0.607 on 16 and 143 DF, p-value: 0.8743
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL5_rank
Effect size...............: -3.245108
Standard error............: 2.655469
Odds ratio (effect size)..: 0.039
Lower 95% CI..............: 0
Upper 95% CI..............: 7.097
T-value...................: -1.222047
P-value...................: 0.2237004
R^2.......................: 0.063602
Adjusted r^2..............: -0.04117
Sample size of AE DB......: 623
Sample size of model......: 160
Missing data %............: 74.31782
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
26.7
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-31.819 -15.990 -5.261 5.452 210.089
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61.035488 105.244620 0.580 0.563
currentDF[, TRAIT] -2.457169 2.513783 -0.977 0.330
Age -0.125314 0.368737 -0.340 0.734
Gendermale 2.795241 6.075628 0.460 0.646
ORdate_epoch -0.002174 0.006981 -0.311 0.756
Hypertension.compositeyes 5.565669 8.135875 0.684 0.495
DiabetesStatusDiabetes -9.566689 6.796799 -1.408 0.161
SmokerStatusEx-smoker -0.146312 5.839922 -0.025 0.980
SmokerStatusNever smoked -6.833897 7.973191 -0.857 0.393
Med.Statin.LLDyes -2.705923 5.696006 -0.475 0.635
Med.all.antiplateletyes -3.818322 9.321445 -0.410 0.683
GFR_MDRD -0.181739 0.156203 -1.163 0.247
BMI -0.099018 0.670927 -0.148 0.883
MedHx_CVDNo 5.896464 5.465876 1.079 0.282
stenose50-70% 9.993737 37.564923 0.266 0.791
stenose70-90% 16.110172 32.631375 0.494 0.622
stenose90-99% 15.843750 32.573331 0.486 0.627
stenose100% (Occlusion) -6.135593 41.121254 -0.149 0.882
Residual standard error: 31.32 on 148 degrees of freedom
Multiple R-squared: 0.05049, Adjusted R-squared: -0.05857
F-statistic: 0.463 on 17 and 148 DF, p-value: 0.9654
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL6_rank
Effect size...............: -2.457169
Standard error............: 2.513783
Odds ratio (effect size)..: 0.086
Lower 95% CI..............: 0.001
Upper 95% CI..............: 11.821
T-value...................: -0.977479
P-value...................: 0.3299273
R^2.......................: 0.050493
Adjusted r^2..............: -0.058572
Sample size of AE DB......: 623
Sample size of model......: 166
Missing data %............: 73.35474
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
47.349 -9.874 -0.277
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-36.181 -14.386 -4.350 7.589 203.858
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 70.768721 104.295273 0.679 0.4986
currentDF[, TRAIT] 1.304865 2.520518 0.518 0.6055
Age -0.266970 0.341119 -0.783 0.4352
Gendermale 0.109966 6.090895 0.018 0.9856
ORdate_epoch -0.001114 0.006942 -0.161 0.8727
Hypertension.compositeyes 4.103920 7.555310 0.543 0.5879
DiabetesStatusDiabetes -11.400345 6.400173 -1.781 0.0771 .
SmokerStatusEx-smoker -1.028139 5.718107 -0.180 0.8576
SmokerStatusNever smoked -6.831397 8.229451 -0.830 0.4079
Med.Statin.LLDyes -3.857459 5.475895 -0.704 0.4824
Med.all.antiplateletyes -2.293451 8.436622 -0.272 0.7862
GFR_MDRD -0.337771 0.134928 -2.503 0.0135 *
BMI -0.110912 0.644698 -0.172 0.8637
MedHx_CVDNo 7.303814 5.337681 1.368 0.1735
stenose50-70% 14.911972 34.954356 0.427 0.6703
stenose70-90% 12.926699 30.526721 0.423 0.6726
stenose90-99% 17.794668 30.415685 0.585 0.5595
stenose100% (Occlusion) -4.196833 43.423601 -0.097 0.9231
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 29.18 on 136 degrees of freedom
Multiple R-squared: 0.08425, Adjusted R-squared: -0.03021
F-statistic: 0.736 on 17 and 136 DF, p-value: 0.7614
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL8_rank
Effect size...............: 1.304865
Standard error............: 2.520518
Odds ratio (effect size)..: 3.687
Lower 95% CI..............: 0.026
Upper 95% CI..............: 515.471
T-value...................: 0.517697
P-value...................: 0.6055106
R^2.......................: 0.084254
Adjusted r^2..............: -0.030215
Sample size of AE DB......: 623
Sample size of model......: 154
Missing data %............: 75.2809
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes MedHx_CVDNo
12.272 4.523 13.449 11.345
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-46.45 -19.40 -6.21 7.60 392.54
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.037e+02 1.274e+02 -0.814 0.4169
currentDF[, TRAIT] 5.515e+00 3.203e+00 1.722 0.0869 .
Age 4.228e-01 4.673e-01 0.905 0.3669
Gendermale 7.743e+00 7.745e+00 1.000 0.3189
ORdate_epoch 5.362e-03 8.325e-03 0.644 0.5204
Hypertension.compositeyes 1.395e+01 1.047e+01 1.333 0.1844
DiabetesStatusDiabetes -1.138e+01 8.657e+00 -1.315 0.1903
SmokerStatusEx-smoker -1.074e+01 7.569e+00 -1.419 0.1578
SmokerStatusNever smoked -1.851e+01 9.639e+00 -1.921 0.0564 .
Med.Statin.LLDyes 3.855e+00 7.599e+00 0.507 0.6125
Med.all.antiplateletyes -2.816e+00 1.264e+01 -0.223 0.8240
GFR_MDRD -1.518e-01 1.758e-01 -0.863 0.3893
BMI 3.090e-01 8.496e-01 0.364 0.7165
MedHx_CVDNo 1.408e+01 6.996e+00 2.013 0.0457 *
stenose50-70% 1.401e+00 5.447e+01 0.026 0.9795
stenose70-90% 2.645e+01 4.414e+01 0.599 0.5498
stenose90-99% 2.485e+01 4.395e+01 0.565 0.5726
stenose100% (Occlusion) 1.655e+01 5.590e+01 0.296 0.7676
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 42.7 on 169 degrees of freedom
Multiple R-squared: 0.08693, Adjusted R-squared: -0.004922
F-statistic: 0.9464 on 17 and 169 DF, p-value: 0.5211
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL9_rank
Effect size...............: 5.515299
Standard error............: 3.203045
Odds ratio (effect size)..: 248.464
Lower 95% CI..............: 0.466
Upper 95% CI..............: 132358.2
T-value...................: 1.721893
P-value...................: 0.08691863
R^2.......................: 0.086926
Adjusted r^2..............: -0.004922
Sample size of AE DB......: 623
Sample size of model......: 187
Missing data %............: 69.98395
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes
27.19 -12.37
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-34.698 -15.064 -4.186 7.093 155.652
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 152.940977 103.255497 1.481 0.1411
currentDF[, TRAIT] -1.610936 2.496055 -0.645 0.5199
Age -0.208530 0.335881 -0.621 0.5358
Gendermale 1.648079 5.928065 0.278 0.7815
ORdate_epoch -0.007795 0.007208 -1.081 0.2816
Hypertension.compositeyes 3.913896 7.856162 0.498 0.6192
DiabetesStatusDiabetes -11.971242 6.484907 -1.846 0.0673 .
SmokerStatusEx-smoker 3.104410 5.486883 0.566 0.5726
SmokerStatusNever smoked -0.393869 7.507139 -0.052 0.9582
Med.Statin.LLDyes -7.210981 5.428428 -1.328 0.1865
Med.all.antiplateletyes -2.707332 8.493664 -0.319 0.7505
GFR_MDRD -0.080901 0.154891 -0.522 0.6024
BMI -0.380631 0.763865 -0.498 0.6192
MedHx_CVDNo 1.032071 5.363695 0.192 0.8477
stenose70-90% 3.997997 20.755707 0.193 0.8476
stenose90-99% 0.575526 20.599537 0.028 0.9778
stenose100% (Occlusion) -24.470011 30.112107 -0.813 0.4180
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 27.28 on 124 degrees of freedom
Multiple R-squared: 0.0718, Adjusted R-squared: -0.04797
F-statistic: 0.5995 on 16 and 124 DF, p-value: 0.8793
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL10_rank
Effect size...............: -1.610936
Standard error............: 2.496055
Odds ratio (effect size)..: 0.2
Lower 95% CI..............: 0.001
Upper 95% CI..............: 26.611
T-value...................: -0.645393
P-value...................: 0.5198651
R^2.......................: 0.071797
Adjusted r^2..............: -0.047971
Sample size of AE DB......: 623
Sample size of model......: 141
Missing data %............: 77.36758
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus, data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes
30.08 -12.16
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-41.843 -17.184 -5.674 7.406 199.835
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 109.081829 119.358629 0.914 0.3624
currentDF[, TRAIT] -3.747386 2.895094 -1.294 0.1978
Age -0.169678 0.399571 -0.425 0.6718
Gendermale 5.644918 6.860809 0.823 0.4121
ORdate_epoch -0.002681 0.008285 -0.324 0.7468
Hypertension.compositeyes 7.127322 9.353193 0.762 0.4474
DiabetesStatusDiabetes -12.919779 7.441690 -1.736 0.0849 .
SmokerStatusEx-smoker -3.107886 6.616386 -0.470 0.6393
SmokerStatusNever smoked -9.175993 8.624990 -1.064 0.2893
Med.Statin.LLDyes -6.169667 6.512821 -0.947 0.3452
Med.all.antiplateletyes -3.602165 9.995524 -0.360 0.7191
GFR_MDRD -0.163398 0.175801 -0.929 0.3544
BMI -0.942798 0.889289 -1.060 0.2910
MedHx_CVDNo 5.208424 6.009919 0.867 0.3877
stenose70-90% 2.097430 24.493718 0.086 0.9319
stenose90-99% -0.292415 24.409199 -0.012 0.9905
stenose100% (Occlusion) -30.015971 42.905802 -0.700 0.4854
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 32.37 on 131 degrees of freedom
Multiple R-squared: 0.07539, Adjusted R-squared: -0.03755
F-statistic: 0.6675 on 16 and 131 DF, p-value: 0.8213
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL12_rank
Effect size...............: -3.747386
Standard error............: 2.895094
Odds ratio (effect size)..: 0.024
Lower 95% CI..............: 0
Upper 95% CI..............: 6.869
T-value...................: -1.294392
P-value...................: 0.1978072
R^2.......................: 0.075385
Adjusted r^2..............: -0.037545
Sample size of AE DB......: 623
Sample size of model......: 148
Missing data %............: 76.24398
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Hypertension.composite + SmokerStatus + MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Hypertension.compositeyes SmokerStatusEx-smoker
-22.894 5.055 0.632 12.210 -9.777
SmokerStatusNever smoked MedHx_CVDNo
-17.437 12.669
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-46.10 -18.81 -7.42 7.90 393.71
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.005e+02 1.207e+02 -0.832 0.4062
currentDF[, TRAIT] 4.207e+00 3.096e+00 1.359 0.1759
Age 4.975e-01 4.299e-01 1.157 0.2486
Gendermale 7.052e+00 7.203e+00 0.979 0.3289
ORdate_epoch 5.358e-03 7.986e-03 0.671 0.5031
Hypertension.compositeyes 1.021e+01 9.420e+00 1.083 0.2800
DiabetesStatusDiabetes -1.056e+01 7.794e+00 -1.355 0.1769
SmokerStatusEx-smoker -1.121e+01 7.033e+00 -1.594 0.1126
SmokerStatusNever smoked -1.824e+01 9.071e+00 -2.011 0.0458 *
Med.Statin.LLDyes 2.851e+00 6.932e+00 0.411 0.6814
Med.all.antiplateletyes -4.766e+00 1.088e+01 -0.438 0.6619
GFR_MDRD -1.358e-01 1.660e-01 -0.818 0.4145
BMI 3.189e-01 7.766e-01 0.411 0.6818
MedHx_CVDNo 1.219e+01 6.359e+00 1.917 0.0567 .
stenose50-70% 1.402e+01 4.944e+01 0.284 0.7770
stenose70-90% 2.285e+01 4.280e+01 0.534 0.5940
stenose90-99% 2.212e+01 4.275e+01 0.517 0.6056
stenose100% (Occlusion) 1.013e+01 5.360e+01 0.189 0.8503
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.41 on 186 degrees of freedom
Multiple R-squared: 0.0741, Adjusted R-squared: -0.01053
F-statistic: 0.8756 on 17 and 186 DF, p-value: 0.6035
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL13_rank
Effect size...............: 4.206555
Standard error............: 3.09586
Odds ratio (effect size)..: 67.125
Lower 95% CI..............: 0.155
Upper 95% CI..............: 28982.27
T-value...................: 1.358768
P-value...................: 0.1758656
R^2.......................: 0.0741
Adjusted r^2..............: -0.010525
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.31 10.03
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-42.28 -18.89 -7.44 6.63 394.89
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.034e+02 1.210e+02 -0.855 0.3937
currentDF[, TRAIT] 3.043e+00 3.074e+00 0.990 0.3234
Age 4.429e-01 4.268e-01 1.038 0.3008
Gendermale 7.166e+00 7.234e+00 0.991 0.3232
ORdate_epoch 5.891e-03 7.984e-03 0.738 0.4616
Hypertension.compositeyes 9.563e+00 9.418e+00 1.015 0.3112
DiabetesStatusDiabetes -1.094e+01 7.802e+00 -1.403 0.1624
SmokerStatusEx-smoker -1.051e+01 7.009e+00 -1.499 0.1354
SmokerStatusNever smoked -1.741e+01 9.053e+00 -1.923 0.0560 .
Med.Statin.LLDyes 2.957e+00 6.955e+00 0.425 0.6712
Med.all.antiplateletyes -4.815e+00 1.091e+01 -0.441 0.6596
GFR_MDRD -1.435e-01 1.663e-01 -0.863 0.3891
BMI 2.841e-01 7.778e-01 0.365 0.7154
MedHx_CVDNo 1.201e+01 6.371e+00 1.886 0.0609 .
stenose50-70% 1.642e+01 4.950e+01 0.332 0.7404
stenose70-90% 2.426e+01 4.288e+01 0.566 0.5723
stenose90-99% 2.394e+01 4.283e+01 0.559 0.5768
stenose100% (Occlusion) 9.047e+00 5.373e+01 0.168 0.8665
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.5 on 186 degrees of freedom
Multiple R-squared: 0.06981, Adjusted R-squared: -0.01521
F-statistic: 0.8211 on 17 and 186 DF, p-value: 0.6674
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IL21_rank
Effect size...............: 3.043012
Standard error............: 3.073602
Odds ratio (effect size)..: 20.968
Lower 95% CI..............: 0.051
Upper 95% CI..............: 8666.942
T-value...................: 0.990048
P-value...................: 0.323437
R^2.......................: 0.069811
Adjusted r^2..............: -0.015206
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
46.116 -10.540 -0.229
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-37.052 -16.810 -6.036 4.841 204.684
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.083e+01 1.162e+02 0.265 0.7911
currentDF[, TRAIT] -8.858e-01 3.003e+00 -0.295 0.7684
Age -1.379e-01 3.821e-01 -0.361 0.7187
Gendermale 3.317e+00 6.981e+00 0.475 0.6354
ORdate_epoch 5.428e-04 7.843e-03 0.069 0.9449
Hypertension.compositeyes 4.378e+00 9.269e+00 0.472 0.6375
DiabetesStatusDiabetes -1.173e+01 7.028e+00 -1.669 0.0973 .
SmokerStatusEx-smoker -7.864e-01 6.341e+00 -0.124 0.9015
SmokerStatusNever smoked -5.697e+00 8.465e+00 -0.673 0.5021
Med.Statin.LLDyes -3.830e+00 6.203e+00 -0.617 0.5379
Med.all.antiplateletyes -1.799e+00 9.179e+00 -0.196 0.8449
GFR_MDRD -2.448e-01 1.541e-01 -1.588 0.1145
BMI -1.435e-01 7.120e-01 -0.202 0.8406
MedHx_CVDNo 6.153e+00 6.026e+00 1.021 0.3090
stenose50-70% 1.502e+01 3.880e+01 0.387 0.6994
stenose70-90% 2.130e+01 3.385e+01 0.629 0.5301
stenose90-99% 1.978e+01 3.366e+01 0.588 0.5577
stenose100% (Occlusion) -2.164e+00 4.783e+01 -0.045 0.9640
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 32.3 on 138 degrees of freedom
Multiple R-squared: 0.05484, Adjusted R-squared: -0.06159
F-statistic: 0.471 on 17 and 138 DF, p-value: 0.9621
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: INFG_rank
Effect size...............: -0.885797
Standard error............: 3.002723
Odds ratio (effect size)..: 0.412
Lower 95% CI..............: 0.001
Upper 95% CI..............: 148.345
T-value...................: -0.294998
P-value...................: 0.7684387
R^2.......................: 0.054838
Adjusted r^2..............: -0.061595
Sample size of AE DB......: 623
Sample size of model......: 156
Missing data %............: 74.95987
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
45.0253 -14.4089 -0.2155
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-36.268 -18.091 -3.865 7.017 200.531
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 117.041754 114.719309 1.020 0.3095
currentDF[, TRAIT] -3.011704 2.882297 -1.045 0.2980
Age -0.148370 0.389891 -0.381 0.7042
Gendermale 4.288191 6.814393 0.629 0.5303
ORdate_epoch -0.002815 0.007968 -0.353 0.7244
Hypertension.compositeyes 8.726960 9.150317 0.954 0.3420
DiabetesStatusDiabetes -14.703110 7.380005 -1.992 0.0484 *
SmokerStatusEx-smoker -0.663673 6.368894 -0.104 0.9172
SmokerStatusNever smoked -7.073530 8.668658 -0.816 0.4160
Med.Statin.LLDyes -6.740318 6.498422 -1.037 0.3016
Med.all.antiplateletyes -6.869815 9.845985 -0.698 0.4866
GFR_MDRD -0.225237 0.168685 -1.335 0.1841
BMI -0.929990 0.834935 -1.114 0.2674
MedHx_CVDNo 4.959465 6.164356 0.805 0.4226
stenose70-90% -0.196921 24.403726 -0.008 0.9936
stenose90-99% -2.378007 24.165617 -0.098 0.9218
stenose100% (Occlusion) -31.350754 42.550894 -0.737 0.4626
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 32.28 on 130 degrees of freedom
Multiple R-squared: 0.08234, Adjusted R-squared: -0.0306
F-statistic: 0.729 on 16 and 130 DF, p-value: 0.7602
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: TNFA_rank
Effect size...............: -3.011704
Standard error............: 2.882297
Odds ratio (effect size)..: 0.049
Lower 95% CI..............: 0
Upper 95% CI..............: 13.98
T-value...................: -1.044897
P-value...................: 0.2980096
R^2.......................: 0.082341
Adjusted r^2..............: -0.030602
Sample size of AE DB......: 623
Sample size of model......: 147
Missing data %............: 76.40449
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.31 10.03
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-40.41 -18.87 -7.19 5.81 393.46
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.245e+02 1.307e+02 -0.952 0.3422
currentDF[, TRAIT] 8.860e-01 3.564e+00 0.249 0.8039
Age 3.822e-01 4.236e-01 0.902 0.3680
Gendermale 7.885e+00 7.213e+00 1.093 0.2757
ORdate_epoch 7.585e-03 8.902e-03 0.852 0.3952
Hypertension.compositeyes 9.409e+00 9.497e+00 0.991 0.3231
DiabetesStatusDiabetes -1.140e+01 7.814e+00 -1.459 0.1464
SmokerStatusEx-smoker -9.582e+00 6.999e+00 -1.369 0.1726
SmokerStatusNever smoked -1.619e+01 9.001e+00 -1.798 0.0738 .
Med.Statin.LLDyes 3.456e+00 6.958e+00 0.497 0.6200
Med.all.antiplateletyes -4.515e+00 1.096e+01 -0.412 0.6808
GFR_MDRD -1.389e-01 1.690e-01 -0.822 0.4119
BMI 2.701e-01 7.798e-01 0.346 0.7294
MedHx_CVDNo 1.183e+01 6.390e+00 1.851 0.0657 .
stenose50-70% 1.956e+01 4.958e+01 0.395 0.6936
stenose70-90% 2.615e+01 4.304e+01 0.607 0.5443
stenose90-99% 2.688e+01 4.297e+01 0.625 0.5324
stenose100% (Occlusion) 9.416e+00 5.391e+01 0.175 0.8615
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.61 on 186 degrees of freedom
Multiple R-squared: 0.06522, Adjusted R-squared: -0.02022
F-statistic: 0.7634 on 17 and 186 DF, p-value: 0.7333
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MIF_rank
Effect size...............: 0.886013
Standard error............: 3.563763
Odds ratio (effect size)..: 2.425
Lower 95% CI..............: 0.002
Upper 95% CI..............: 2620.156
T-value...................: 0.248617
P-value...................: 0.8039312
R^2.......................: 0.06522
Adjusted r^2..............: -0.020217
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.36 10.14
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-41.50 -18.36 -7.09 6.52 393.46
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.175e+02 1.248e+02 -0.942 0.3474
currentDF[, TRAIT] 8.126e-01 3.051e+00 0.266 0.7903
Age 4.180e-01 4.306e-01 0.971 0.3329
Gendermale 7.399e+00 7.333e+00 1.009 0.3143
ORdate_epoch 6.965e-03 8.221e-03 0.847 0.3980
Hypertension.compositeyes 9.966e+00 9.557e+00 1.043 0.2984
DiabetesStatusDiabetes -1.158e+01 7.876e+00 -1.470 0.1432
SmokerStatusEx-smoker -9.844e+00 7.032e+00 -1.400 0.1632
SmokerStatusNever smoked -1.665e+01 9.084e+00 -1.833 0.0684 .
Med.Statin.LLDyes 3.631e+00 7.017e+00 0.517 0.6055
Med.all.antiplateletyes -7.078e+00 1.165e+01 -0.607 0.5444
GFR_MDRD -1.415e-01 1.682e-01 -0.841 0.4012
BMI 2.905e-01 7.954e-01 0.365 0.7154
MedHx_CVDNo 1.187e+01 6.453e+00 1.839 0.0675 .
stenose50-70% 2.040e+01 4.967e+01 0.411 0.6818
stenose70-90% 2.665e+01 4.309e+01 0.618 0.5370
stenose90-99% 2.758e+01 4.294e+01 0.642 0.5215
stenose100% (Occlusion) 8.111e+00 5.425e+01 0.150 0.8813
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.78 on 184 degrees of freedom
Multiple R-squared: 0.06711, Adjusted R-squared: -0.01908
F-statistic: 0.7786 on 17 and 184 DF, p-value: 0.7162
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MCP1_rank
Effect size...............: 0.812621
Standard error............: 3.051377
Odds ratio (effect size)..: 2.254
Lower 95% CI..............: 0.006
Upper 95% CI..............: 891.87
T-value...................: 0.266313
P-value...................: 0.7902964
R^2.......................: 0.067111
Adjusted r^2..............: -0.01908
Sample size of AE DB......: 623
Sample size of model......: 202
Missing data %............: 67.57624
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.23 11.12
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-41.25 -19.78 -6.72 7.11 394.54
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -85.526838 126.232255 -0.678 0.4990
currentDF[, TRAIT] 2.269791 3.236998 0.701 0.4841
Age 0.343128 0.449992 0.763 0.4468
Gendermale 8.165880 7.773708 1.050 0.2950
ORdate_epoch 0.004457 0.008310 0.536 0.5924
Hypertension.compositeyes 12.730777 10.186535 1.250 0.2131
DiabetesStatusDiabetes -11.992625 8.395786 -1.428 0.1550
SmokerStatusEx-smoker -9.667751 7.524746 -1.285 0.2006
SmokerStatusNever smoked -17.391007 9.667135 -1.799 0.0738 .
Med.Statin.LLDyes 3.616432 7.456169 0.485 0.6283
Med.all.antiplateletyes -3.413065 12.601680 -0.271 0.7868
GFR_MDRD -0.138338 0.175293 -0.789 0.4311
BMI 0.308087 0.828785 0.372 0.7105
MedHx_CVDNo 13.750137 6.971626 1.972 0.0502 .
stenose50-70% 3.492875 54.689881 0.064 0.9492
stenose70-90% 25.096803 44.286292 0.567 0.5717
stenose90-99% 23.838308 44.215146 0.539 0.5905
stenose100% (Occlusion) 8.551463 55.763091 0.153 0.8783
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 42.7 on 172 degrees of freedom
Multiple R-squared: 0.07343, Adjusted R-squared: -0.01815
F-statistic: 0.8018 on 17 and 172 DF, p-value: 0.6895
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MIP1a_rank
Effect size...............: 2.269791
Standard error............: 3.236998
Odds ratio (effect size)..: 9.677
Lower 95% CI..............: 0.017
Upper 95% CI..............: 5509.926
T-value...................: 0.701203
P-value...................: 0.4841242
R^2.......................: 0.073432
Adjusted r^2..............: -0.018148
Sample size of AE DB......: 623
Sample size of model......: 190
Missing data %............: 69.50241
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
ORdate_epoch + Hypertension.composite + SmokerStatus + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age ORdate_epoch Hypertension.compositeyes
-205.13985 9.15454 0.72013 0.01372 15.23186
SmokerStatusEx-smoker SmokerStatusNever smoked MedHx_CVDNo
-7.86001 -17.92475 14.84256
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-42.44 -17.98 -7.91 8.01 368.77
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.118e+02 1.277e+02 -1.659 0.0989 .
currentDF[, TRAIT] 8.268e+00 3.335e+00 2.479 0.0141 *
Age 5.564e-01 4.284e-01 1.299 0.1956
Gendermale 6.773e+00 7.323e+00 0.925 0.3562
ORdate_epoch 1.395e-02 8.572e-03 1.628 0.1053
Hypertension.compositeyes 1.341e+01 9.725e+00 1.378 0.1697
DiabetesStatusDiabetes -7.535e+00 7.924e+00 -0.951 0.3429
SmokerStatusEx-smoker -9.319e+00 7.072e+00 -1.318 0.1892
SmokerStatusNever smoked -1.856e+01 9.023e+00 -2.057 0.0411 *
Med.Statin.LLDyes 3.608e+00 6.955e+00 0.519 0.6045
Med.all.antiplateletyes -5.085e+00 1.144e+01 -0.445 0.6571
GFR_MDRD -1.113e-01 1.667e-01 -0.667 0.5054
BMI 1.375e-01 7.872e-01 0.175 0.8615
MedHx_CVDNo 1.506e+01 6.538e+00 2.303 0.0224 *
stenose50-70% 4.190e+00 5.238e+01 0.080 0.9363
stenose70-90% 1.909e+01 4.273e+01 0.447 0.6556
stenose90-99% 2.049e+01 4.251e+01 0.482 0.6304
stenose100% (Occlusion) -1.790e-01 5.389e+01 -0.003 0.9974
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.25 on 182 degrees of freedom
Multiple R-squared: 0.09999, Adjusted R-squared: 0.01592
F-statistic: 1.189 on 17 and 182 DF, p-value: 0.2768
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: RANTES_rank
Effect size...............: 8.267776
Standard error............: 3.335321
Odds ratio (effect size)..: 3896.273
Lower 95% CI..............: 5.644
Upper 95% CI..............: 2689873
T-value...................: 2.478855
P-value...................: 0.01409078
R^2.......................: 0.099991
Adjusted r^2..............: 0.015924
Sample size of AE DB......: 623
Sample size of model......: 200
Missing data %............: 67.89727
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Hypertension.composite + SmokerStatus + MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Hypertension.compositeyes SmokerStatusEx-smoker
-24.499 5.257 0.646 12.910 -9.580
SmokerStatusNever smoked MedHx_CVDNo
-17.302 12.401
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-43.07 -18.74 -7.04 7.30 393.90
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -64.778470 126.441970 -0.512 0.6090
currentDF[, TRAIT] 4.673991 3.404447 1.373 0.1715
Age 0.466731 0.434350 1.075 0.2840
Gendermale 7.037912 7.367591 0.955 0.3407
ORdate_epoch 0.002583 0.008488 0.304 0.7613
Hypertension.compositeyes 11.369194 9.772964 1.163 0.2462
DiabetesStatusDiabetes -10.183391 7.991823 -1.274 0.2042
SmokerStatusEx-smoker -11.518984 7.149698 -1.611 0.1089
SmokerStatusNever smoked -18.393576 9.358234 -1.965 0.0509 .
Med.Statin.LLDyes 2.638366 7.107668 0.371 0.7109
Med.all.antiplateletyes -3.879083 11.231167 -0.345 0.7302
GFR_MDRD -0.153723 0.168678 -0.911 0.3633
BMI 0.305130 0.787065 0.388 0.6987
MedHx_CVDNo 12.447058 6.544033 1.902 0.0587 .
stenose50-70% 2.648918 53.405992 0.050 0.9605
stenose70-90% 23.946012 43.193055 0.554 0.5800
stenose90-99% 23.289951 43.097083 0.540 0.5896
stenose100% (Occlusion) 9.451362 54.270501 0.174 0.8619
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.79 on 182 degrees of freedom
Multiple R-squared: 0.07496, Adjusted R-squared: -0.01145
F-statistic: 0.8675 on 17 and 182 DF, p-value: 0.613
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MIG_rank
Effect size...............: 4.673991
Standard error............: 3.404447
Odds ratio (effect size)..: 107.124
Lower 95% CI..............: 0.136
Upper 95% CI..............: 84686.07
T-value...................: 1.372908
P-value...................: 0.1714703
R^2.......................: 0.074957
Adjusted r^2..............: -0.011448
Sample size of AE DB......: 623
Sample size of model......: 200
Missing data %............: 67.89727
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Hypertension.composite + SmokerStatus + MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Hypertension.compositeyes SmokerStatusEx-smoker
-27.6393 5.2772 0.6609 16.4819 -10.7170
SmokerStatusNever smoked MedHx_CVDNo
-18.1202 12.2824
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-47.72 -19.34 -7.02 6.42 392.70
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.248e+02 1.286e+02 -0.970 0.3334
currentDF[, TRAIT] 4.887e+00 3.441e+00 1.420 0.1575
Age 4.895e-01 4.710e-01 1.039 0.3001
Gendermale 9.369e+00 7.725e+00 1.213 0.2270
ORdate_epoch 6.635e-03 8.449e-03 0.785 0.4334
Hypertension.compositeyes 1.398e+01 1.047e+01 1.336 0.1835
DiabetesStatusDiabetes -1.124e+01 8.755e+00 -1.284 0.2011
SmokerStatusEx-smoker -1.325e+01 7.796e+00 -1.699 0.0912 .
SmokerStatusNever smoked -1.933e+01 1.005e+01 -1.923 0.0562 .
Med.Statin.LLDyes 3.657e+00 7.685e+00 0.476 0.6348
Med.all.antiplateletyes -3.083e+00 1.197e+01 -0.258 0.7971
GFR_MDRD -1.399e-01 1.838e-01 -0.761 0.4476
BMI 3.432e-01 8.317e-01 0.413 0.6804
MedHx_CVDNo 1.226e+01 7.081e+00 1.732 0.0852 .
stenose50-70% 2.500e+00 5.512e+01 0.045 0.9639
stenose70-90% 2.508e+01 4.471e+01 0.561 0.5755
stenose90-99% 2.539e+01 4.447e+01 0.571 0.5688
stenose100% (Occlusion) 1.752e+01 5.617e+01 0.312 0.7555
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 43.1 on 166 degrees of freedom
Multiple R-squared: 0.08357, Adjusted R-squared: -0.01028
F-statistic: 0.8905 on 17 and 166 DF, p-value: 0.586
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: IP10_rank
Effect size...............: 4.886577
Standard error............: 3.441477
Odds ratio (effect size)..: 132.499
Lower 95% CI..............: 0.156
Upper 95% CI..............: 112630.9
T-value...................: 1.419907
P-value...................: 0.15751
R^2.......................: 0.083572
Adjusted r^2..............: -0.010279
Sample size of AE DB......: 623
Sample size of model......: 184
Missing data %............: 70.46549
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.31 10.03
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-40.87 -18.39 -7.49 5.71 394.27
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.048e+02 1.225e+02 -0.855 0.3937
currentDF[, TRAIT] 1.168e+00 3.124e+00 0.374 0.7089
Age 3.957e-01 4.262e-01 0.928 0.3544
Gendermale 7.624e+00 7.262e+00 1.050 0.2951
ORdate_epoch 6.024e-03 8.118e-03 0.742 0.4590
Hypertension.compositeyes 9.114e+00 9.430e+00 0.967 0.3350
DiabetesStatusDiabetes -1.129e+01 7.821e+00 -1.443 0.1506
SmokerStatusEx-smoker -9.985e+00 7.016e+00 -1.423 0.1564
SmokerStatusNever smoked -1.651e+01 9.072e+00 -1.820 0.0704 .
Med.Statin.LLDyes 3.442e+00 6.951e+00 0.495 0.6211
Med.all.antiplateletyes -4.551e+00 1.095e+01 -0.416 0.6781
GFR_MDRD -1.437e-01 1.667e-01 -0.862 0.3898
BMI 2.825e-01 7.798e-01 0.362 0.7176
MedHx_CVDNo 1.188e+01 6.390e+00 1.859 0.0646 .
stenose50-70% 1.921e+01 4.955e+01 0.388 0.6987
stenose70-90% 2.596e+01 4.298e+01 0.604 0.5466
stenose90-99% 2.641e+01 4.294e+01 0.615 0.5392
stenose100% (Occlusion) 9.080e+00 5.390e+01 0.168 0.8664
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.6 on 186 degrees of freedom
Multiple R-squared: 0.06561, Adjusted R-squared: -0.01979
F-statistic: 0.7683 on 17 and 186 DF, p-value: 0.7278
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 1.167919
Standard error............: 3.12353
Odds ratio (effect size)..: 3.215
Lower 95% CI..............: 0.007
Upper 95% CI..............: 1465.626
T-value...................: 0.37391
P-value...................: 0.7088973
R^2.......................: 0.065612
Adjusted r^2..............: -0.019789
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age MedHx_CVDNo
-22.834 4.952 0.687 13.560
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-51.99 -18.84 -7.23 7.33 383.80
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -246.43653 150.53202 -1.637 0.1035
currentDF[, TRAIT] 6.53108 3.59260 1.818 0.0709 .
Age 0.77876 0.47939 1.624 0.1062
Gendermale 12.93610 8.08822 1.599 0.1117
ORdate_epoch 0.01447 0.01039 1.393 0.1655
Hypertension.compositeyes 10.37972 10.19722 1.018 0.3102
DiabetesStatusDiabetes -10.83813 8.54016 -1.269 0.2062
SmokerStatusEx-smoker -10.06849 7.74560 -1.300 0.1955
SmokerStatusNever smoked -20.09032 9.76266 -2.058 0.0412 *
Med.Statin.LLDyes 3.16041 7.94842 0.398 0.6914
Med.all.antiplateletyes -3.14640 12.46589 -0.252 0.8011
GFR_MDRD -0.11128 0.18625 -0.597 0.5510
BMI 0.21495 0.85844 0.250 0.8026
MedHx_CVDNo 14.53232 7.09559 2.048 0.0422 *
stenose50-70% 28.87864 54.61610 0.529 0.5977
stenose70-90% 31.04845 44.62102 0.696 0.4875
stenose90-99% 26.93155 44.47606 0.606 0.5457
stenose100% (Occlusion) 19.24844 56.62469 0.340 0.7343
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 43.14 on 162 degrees of freedom
Multiple R-squared: 0.1014, Adjusted R-squared: 0.007075
F-statistic: 1.075 on 17 and 162 DF, p-value: 0.3824
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: TARC_rank
Effect size...............: 6.531079
Standard error............: 3.592599
Odds ratio (effect size)..: 686.138
Lower 95% CI..............: 0.6
Upper 95% CI..............: 784320.6
T-value...................: 1.817926
P-value...................: 0.07092285
R^2.......................: 0.101375
Adjusted r^2..............: 0.007075
Sample size of AE DB......: 623
Sample size of model......: 180
Missing data %............: 71.10754
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.31 10.03
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-40.77 -18.15 -7.41 7.22 393.04
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.737e+02 1.298e+02 -1.338 0.1824
currentDF[, TRAIT] 4.192e+00 3.292e+00 1.273 0.2045
Age 4.242e-01 4.222e-01 1.005 0.3163
Gendermale 8.315e+00 7.182e+00 1.158 0.2484
ORdate_epoch 1.074e-02 8.579e-03 1.252 0.2121
Hypertension.compositeyes 1.101e+01 9.507e+00 1.158 0.2483
DiabetesStatusDiabetes -1.092e+01 7.781e+00 -1.404 0.1620
SmokerStatusEx-smoker -1.004e+01 6.955e+00 -1.444 0.1504
SmokerStatusNever smoked -1.768e+01 9.021e+00 -1.960 0.0515 .
Med.Statin.LLDyes 3.120e+00 6.925e+00 0.451 0.6528
Med.all.antiplateletyes -3.401e+00 1.090e+01 -0.312 0.7555
GFR_MDRD -1.185e-01 1.674e-01 -0.708 0.4799
BMI 3.792e-01 7.808e-01 0.486 0.6277
MedHx_CVDNo 1.140e+01 6.360e+00 1.792 0.0747 .
stenose50-70% 2.035e+01 4.924e+01 0.413 0.6799
stenose70-90% 2.601e+01 4.272e+01 0.609 0.5433
stenose90-99% 2.742e+01 4.256e+01 0.644 0.5203
stenose100% (Occlusion) 1.530e+01 5.379e+01 0.284 0.7764
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.43 on 186 degrees of freedom
Multiple R-squared: 0.07299, Adjusted R-squared: -0.01174
F-statistic: 0.8615 on 17 and 186 DF, p-value: 0.6201
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: PARC_rank
Effect size...............: 4.191655
Standard error............: 3.291742
Odds ratio (effect size)..: 66.132
Lower 95% CI..............: 0.104
Upper 95% CI..............: 41917.97
T-value...................: 1.273385
P-value...................: 0.2044705
R^2.......................: 0.072991
Adjusted r^2..............: -0.011736
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.69 10.66
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-41.50 -19.63 -7.43 5.70 392.76
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.182e+02 1.306e+02 -0.905 0.3669
currentDF[, TRAIT] 2.015e+00 3.426e+00 0.588 0.5573
Age 3.804e-01 4.500e-01 0.845 0.3991
Gendermale 8.941e+00 7.746e+00 1.154 0.2500
ORdate_epoch 6.451e-03 8.710e-03 0.741 0.4599
Hypertension.compositeyes 1.327e+01 1.021e+01 1.299 0.1957
DiabetesStatusDiabetes -1.044e+01 8.495e+00 -1.229 0.2207
SmokerStatusEx-smoker -9.978e+00 7.510e+00 -1.329 0.1857
SmokerStatusNever smoked -1.774e+01 9.666e+00 -1.835 0.0682 .
Med.Statin.LLDyes 3.631e+00 7.553e+00 0.481 0.6313
Med.all.antiplateletyes -2.351e+00 1.267e+01 -0.186 0.8530
GFR_MDRD -1.159e-01 1.752e-01 -0.662 0.5091
BMI 3.088e-01 8.351e-01 0.370 0.7120
MedHx_CVDNo 1.345e+01 6.992e+00 1.924 0.0560 .
stenose50-70% 3.757e+00 5.489e+01 0.068 0.9455
stenose70-90% 2.636e+01 4.435e+01 0.594 0.5530
stenose90-99% 2.585e+01 4.420e+01 0.585 0.5594
stenose100% (Occlusion) 1.077e+01 5.589e+01 0.193 0.8474
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 42.83 on 172 degrees of freedom
Multiple R-squared: 0.07118, Adjusted R-squared: -0.02062
F-statistic: 0.7754 on 17 and 172 DF, p-value: 0.7195
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MDC_rank
Effect size...............: 2.014567
Standard error............: 3.425852
Odds ratio (effect size)..: 7.497
Lower 95% CI..............: 0.009
Upper 95% CI..............: 6181.014
T-value...................: 0.588049
P-value...................: 0.557271
R^2.......................: 0.071183
Adjusted r^2..............: -0.020619
Sample size of AE DB......: 623
Sample size of model......: 190
Missing data %............: 69.50241
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Hypertension.composite + SmokerStatus + MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Hypertension.compositeyes SmokerStatusEx-smoker
-22.3186 4.7969 0.6208 12.3750 -9.4542
SmokerStatusNever smoked MedHx_CVDNo
-16.3703 12.1656
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-46.26 -18.37 -7.62 6.21 386.12
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.222e+02 1.204e+02 -1.015 0.3115
currentDF[, TRAIT] 4.629e+00 3.003e+00 1.542 0.1249
Age 4.883e-01 4.260e-01 1.146 0.2531
Gendermale 7.339e+00 7.173e+00 1.023 0.3076
ORdate_epoch 6.686e-03 7.923e-03 0.844 0.3998
Hypertension.compositeyes 1.038e+01 9.408e+00 1.103 0.2713
DiabetesStatusDiabetes -1.124e+01 7.754e+00 -1.450 0.1488
SmokerStatusEx-smoker -1.122e+01 7.005e+00 -1.601 0.1110
SmokerStatusNever smoked -1.767e+01 8.971e+00 -1.970 0.0503 .
Med.Statin.LLDyes 3.019e+00 6.911e+00 0.437 0.6628
Med.all.antiplateletyes -5.778e+00 1.090e+01 -0.530 0.5968
GFR_MDRD -1.446e-01 1.656e-01 -0.873 0.3840
BMI 3.763e-01 7.777e-01 0.484 0.6290
MedHx_CVDNo 1.160e+01 6.342e+00 1.829 0.0690 .
stenose50-70% 1.850e+01 4.916e+01 0.376 0.7071
stenose70-90% 2.790e+01 4.263e+01 0.654 0.5136
stenose90-99% 2.795e+01 4.248e+01 0.658 0.5114
stenose100% (Occlusion) 1.650e+01 5.368e+01 0.307 0.7589
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.35 on 186 degrees of freedom
Multiple R-squared: 0.07671, Adjusted R-squared: -0.007682
F-statistic: 0.909 on 17 and 186 DF, p-value: 0.5643
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: OPG_rank
Effect size...............: 4.628562
Standard error............: 3.00256
Odds ratio (effect size)..: 102.367
Lower 95% CI..............: 0.285
Upper 95% CI..............: 36812.04
T-value...................: 1.541538
P-value...................: 0.1248854
R^2.......................: 0.076705
Adjusted r^2..............: -0.007682
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.31 10.03
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-40.83 -18.46 -7.20 5.33 392.59
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -99.007162 123.792020 -0.800 0.4249
currentDF[, TRAIT] -1.554682 3.153846 -0.493 0.6226
Age 0.329154 0.431170 0.763 0.4462
Gendermale 8.060260 7.205466 1.119 0.2647
ORdate_epoch 0.005674 0.008186 0.693 0.4891
Hypertension.compositeyes 8.643379 9.478823 0.912 0.3630
DiabetesStatusDiabetes -11.791330 7.816061 -1.509 0.1331
SmokerStatusEx-smoker -9.582201 6.980929 -1.373 0.1715
SmokerStatusNever smoked -15.281140 9.061048 -1.686 0.0934 .
Med.Statin.LLDyes 3.628623 6.943220 0.523 0.6019
Med.all.antiplateletyes -4.152514 10.926391 -0.380 0.7043
GFR_MDRD -0.154310 0.167481 -0.921 0.3581
BMI 0.287153 0.779702 0.368 0.7131
MedHx_CVDNo 11.424561 6.409624 1.782 0.0763 .
stenose50-70% 21.552243 49.476901 0.436 0.6636
stenose70-90% 29.044356 43.056954 0.675 0.5008
stenose90-99% 29.881913 42.893386 0.697 0.4869
stenose100% (Occlusion) 11.543342 53.911690 0.214 0.8307
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.59 on 186 degrees of freedom
Multiple R-squared: 0.06613, Adjusted R-squared: -0.01922
F-statistic: 0.7748 on 17 and 186 DF, p-value: 0.7205
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: sICAM1_rank
Effect size...............: -1.554682
Standard error............: 3.153846
Odds ratio (effect size)..: 0.211
Lower 95% CI..............: 0
Upper 95% CI..............: 102.192
T-value...................: -0.492948
P-value...................: 0.6226308
R^2.......................: 0.066129
Adjusted r^2..............: -0.019224
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
44.1105 -7.9409 -0.2219
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-33.641 -16.006 -5.282 7.515 203.674
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.268475 94.897400 0.561 0.575
currentDF[, TRAIT] 0.395968 2.540890 0.156 0.876
Age 0.152157 0.332547 0.458 0.648
Gendermale 4.713248 5.638215 0.836 0.404
ORdate_epoch -0.002097 0.007054 -0.297 0.767
Hypertension.compositeyes 6.418088 7.533300 0.852 0.396
DiabetesStatusDiabetes -7.102697 6.049006 -1.174 0.242
SmokerStatusEx-smoker -1.877933 5.377992 -0.349 0.727
SmokerStatusNever smoked -4.903360 7.365528 -0.666 0.507
Med.Statin.LLDyes -0.561638 5.406214 -0.104 0.917
Med.all.antiplateletyes -4.063880 8.074917 -0.503 0.615
GFR_MDRD -0.190908 0.119530 -1.597 0.112
BMI -0.369760 0.618446 -0.598 0.551
MedHx_CVDNo 7.346535 5.021120 1.463 0.145
stenose70-90% 10.355581 23.162381 0.447 0.655
stenose90-99% 7.628656 22.946463 0.332 0.740
stenose100% (Occlusion) -13.255740 32.666891 -0.406 0.685
Residual standard error: 30.2 on 160 degrees of freedom
Multiple R-squared: 0.06544, Adjusted R-squared: -0.02802
F-statistic: 0.7002 on 16 and 160 DF, p-value: 0.7909
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.395968
Standard error............: 2.54089
Odds ratio (effect size)..: 1.486
Lower 95% CI..............: 0.01
Upper 95% CI..............: 216.18
T-value...................: 0.155838
P-value...................: 0.8763568
R^2.......................: 0.065436
Adjusted r^2..............: -0.028021
Sample size of AE DB......: 623
Sample size of model......: 177
Missing data %............: 71.58908
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) MedHx_CVDNo
24.654 9.944
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-42.14 -18.17 -7.28 6.40 392.39
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.023e+02 1.203e+02 -0.850 0.3963
currentDF[, TRAIT] 1.746e+00 3.054e+00 0.572 0.5683
Age 3.636e-01 4.274e-01 0.851 0.3960
Gendermale 7.756e+00 7.161e+00 1.083 0.2802
ORdate_epoch 6.208e-03 7.978e-03 0.778 0.4375
Hypertension.compositeyes 9.095e+00 9.321e+00 0.976 0.3304
DiabetesStatusDiabetes -1.127e+01 7.597e+00 -1.483 0.1397
SmokerStatusEx-smoker -8.887e+00 6.979e+00 -1.273 0.2045
SmokerStatusNever smoked -1.596e+01 9.262e+00 -1.723 0.0865 .
Med.Statin.LLDyes 3.973e+00 6.963e+00 0.571 0.5689
Med.all.antiplateletyes -4.516e+00 1.125e+01 -0.401 0.6886
GFR_MDRD -1.505e-01 1.669e-01 -0.902 0.3683
BMI 2.097e-01 7.890e-01 0.266 0.7907
MedHx_CVDNo 1.211e+01 6.393e+00 1.894 0.0598 .
stenose50-70% 1.668e+01 4.979e+01 0.335 0.7380
stenose70-90% 2.503e+01 4.300e+01 0.582 0.5613
stenose90-99% 2.557e+01 4.286e+01 0.597 0.5515
stenose100% (Occlusion) 4.544e+00 5.437e+01 0.084 0.9335
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 41.61 on 186 degrees of freedom
Multiple R-squared: 0.06575, Adjusted R-squared: -0.01964
F-statistic: 0.77 on 17 and 186 DF, p-value: 0.7259
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: TGFB_rank
Effect size...............: 1.745713
Standard error............: 3.054339
Odds ratio (effect size)..: 5.73
Lower 95% CI..............: 0.014
Upper 95% CI..............: 2280.654
T-value...................: 0.571552
P-value...................: 0.5683157
R^2.......................: 0.065745
Adjusted r^2..............: -0.019644
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
42.2734 -7.3574 -0.2098
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-32.15 -15.43 -5.30 6.99 208.83
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 74.018797 86.507857 0.856 0.3933
currentDF[, TRAIT] -2.090658 2.200871 -0.950 0.3434
Age -0.067845 0.294986 -0.230 0.8183
Gendermale 1.951811 4.996768 0.391 0.6965
ORdate_epoch -0.003096 0.005689 -0.544 0.5869
Hypertension.compositeyes 6.159844 6.762394 0.911 0.3635
DiabetesStatusDiabetes -7.744756 5.411505 -1.431 0.1541
SmokerStatusEx-smoker -0.817478 4.894311 -0.167 0.8675
SmokerStatusNever smoked -6.332275 6.395858 -0.990 0.3234
Med.Statin.LLDyes -1.140844 4.816423 -0.237 0.8130
Med.all.antiplateletyes -5.443826 7.629261 -0.714 0.4764
GFR_MDRD -0.210949 0.112666 -1.872 0.0627 .
BMI -0.181914 0.561272 -0.324 0.7462
MedHx_CVDNo 5.953441 4.473470 1.331 0.1849
stenose50-70% 12.493186 34.585953 0.361 0.7183
stenose70-90% 16.397904 30.017654 0.546 0.5855
stenose90-99% 14.369162 29.895341 0.481 0.6313
stenose100% (Occlusion) -8.503293 37.713491 -0.225 0.8219
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 29.07 on 186 degrees of freedom
Multiple R-squared: 0.06243, Adjusted R-squared: -0.02326
F-statistic: 0.7286 on 17 and 186 DF, p-value: 0.771
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MMP2_rank
Effect size...............: -2.090658
Standard error............: 2.200871
Odds ratio (effect size)..: 0.124
Lower 95% CI..............: 0.002
Upper 95% CI..............: 9.235
T-value...................: -0.949923
P-value...................: 0.343384
R^2.......................: 0.062433
Adjusted r^2..............: -0.023258
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
42.2734 -7.3574 -0.2098
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-33.852 -15.970 -6.054 5.975 208.385
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 63.844569 85.362558 0.748 0.455
currentDF[, TRAIT] 1.511752 2.137264 0.707 0.480
Age -0.024978 0.294538 -0.085 0.933
Gendermale 1.971742 5.031919 0.392 0.696
ORdate_epoch -0.002342 0.005615 -0.417 0.677
Hypertension.compositeyes 7.187075 6.679536 1.076 0.283
DiabetesStatusDiabetes -7.378232 5.458786 -1.352 0.178
SmokerStatusEx-smoker -0.952355 4.895004 -0.195 0.846
SmokerStatusNever smoked -7.114988 6.337931 -1.123 0.263
Med.Statin.LLDyes -1.136474 4.821831 -0.236 0.814
Med.all.antiplateletyes -5.592372 7.634365 -0.733 0.465
GFR_MDRD -0.185300 0.113467 -1.633 0.104
BMI -0.173248 0.562203 -0.308 0.758
MedHx_CVDNo 6.357757 4.496734 1.414 0.159
stenose50-70% 7.596491 34.874121 0.218 0.828
stenose70-90% 10.942004 30.755176 0.356 0.722
stenose90-99% 9.530358 30.506194 0.312 0.755
stenose100% (Occlusion) -13.415128 38.433311 -0.349 0.727
Residual standard error: 29.1 on 186 degrees of freedom
Multiple R-squared: 0.06041, Adjusted R-squared: -0.02546
F-statistic: 0.7035 on 17 and 186 DF, p-value: 0.797
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MMP8_rank
Effect size...............: 1.511752
Standard error............: 2.137264
Odds ratio (effect size)..: 4.535
Lower 95% CI..............: 0.069
Upper 95% CI..............: 299.104
T-value...................: 0.707331
P-value...................: 0.4802463
R^2.......................: 0.060412
Adjusted r^2..............: -0.025464
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) DiabetesStatusDiabetes GFR_MDRD
42.2734 -7.3574 -0.2098
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-33.821 -15.997 -5.084 6.144 207.615
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 47.913532 85.334448 0.561 0.575
currentDF[, TRAIT] 2.347440 2.094236 1.121 0.264
Age -0.002189 0.295112 -0.007 0.994
Gendermale 2.259763 4.956587 0.456 0.649
ORdate_epoch -0.001518 0.005628 -0.270 0.788
Hypertension.compositeyes 8.495942 6.764760 1.256 0.211
DiabetesStatusDiabetes -7.127082 5.444518 -1.309 0.192
SmokerStatusEx-smoker -1.279841 4.878789 -0.262 0.793
SmokerStatusNever smoked -7.564734 6.332166 -1.195 0.234
Med.Statin.LLDyes -1.385048 4.815149 -0.288 0.774
Med.all.antiplateletyes -6.358506 7.621269 -0.834 0.405
GFR_MDRD -0.175186 0.113635 -1.542 0.125
BMI -0.167655 0.559435 -0.300 0.765
MedHx_CVDNo 6.415670 4.479146 1.432 0.154
stenose50-70% 10.036623 34.514516 0.291 0.772
stenose70-90% 13.989492 30.012706 0.466 0.642
stenose90-99% 12.317485 29.886655 0.412 0.681
stenose100% (Occlusion) -8.864009 37.680419 -0.235 0.814
Residual standard error: 29.04 on 186 degrees of freedom
Multiple R-squared: 0.06421, Adjusted R-squared: -0.02132
F-statistic: 0.7507 on 17 and 186 DF, p-value: 0.7472
Analyzing in dataset ' AEDB.CEA ' the association of ' HDAC9 ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: HDAC9
Trait/outcome.............: MMP9_rank
Effect size...............: 2.34744
Standard error............: 2.094236
Odds ratio (effect size)..: 10.459
Lower 95% CI..............: 0.173
Upper 95% CI..............: 634.06
T-value...................: 1.120905
P-value...................: 0.2637735
R^2.......................: 0.064206
Adjusted r^2..............: -0.021324
Sample size of AE DB......: 623
Sample size of model......: 204
Missing data %............: 67.25522 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)HDAC9 levels vs. vulnerability index
Here we plot the levels of inverse-rank normal transformed target(s)
plaque levels from experiment 1 and 2 to the
Plaque vulnerability index.
library(sjlabelled)
AERNASE.clin.hdac9$yeartemp <- as.numeric(year(AERNASE.clin.hdac9$dateok))
attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"ORyearGroup"] <- NA
AERNASE.clin.hdac9$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin.hdac9$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin.hdac9)
table(AERNASE.clin.hdac9$ORyearGroup, AERNASE.clin.hdac9$ORdate_year)
No data available/missing 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
< 2007 0 32 62 66 82 85 67 0 0 0 0 0 0 0 0 0 0 0 0
> 2007 0 0 0 0 0 0 0 63 67 34 31 22 5 3 3 1 0 0 0Visualisations
# Global test
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter",
add.params = list(size = 2, jitter = 0.2)) +
stat_compare_means(label = "p.format", method = "kruskal.test") +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9_max100",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter",
add.params = list(size = 2, jitter = 0.2)) +
stat_compare_means(label = "p.format", method = "kruskal.test") +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 12 x 8 in image# Global test
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
col = "Plaque_Vulnerability_Index",
fill = "Plaque_Vulnerability_Index",
palette = "npg",
add = "median_iqr", error.plot = "upper_errorbar") +
stat_compare_means(label = "p.format", method = "kruskal.test",
label.x = 1, label.y = 50) +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.BarPlot.median_iqr.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9_max100",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
col = "Plaque_Vulnerability_Index",
fill = "Plaque_Vulnerability_Index",
palette = "npg",
add = "median_iqr", error.plot = "upper_errorbar") +
stat_compare_means(label = "p.format", method = "kruskal.test",
label.x = 1, label.y = 50) +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex.BarPlot.median_iqr.pdf"), plot = last_plot())Saving 12 x 8 in imagep1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
col = "Plaque_Vulnerability_Index",
fill = "Plaque_Vulnerability_Index",
palette = "npg",
add = "mean_se", error.plot = "upper_errorbar") +
stat_compare_means(label = "p.format", method = "kruskal.test",
label.x = 1, label.y = 50) +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.BarPlot.means_se.pdf"), plot = last_plot())Saving 12 x 8 in imagep1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9_max100",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
col = "Plaque_Vulnerability_Index",
fill = "Plaque_Vulnerability_Index",
palette = "npg",
add = "mean_se", error.plot = "upper_errorbar") +
stat_compare_means(label = "p.format", method = "kruskal.test",
label.x = 1, label.y = 50) +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex.BarPlot.means_se.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = subset(AERNASE.clin.hdac9, HDAC9 <100), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(subset(AERNASE.clin.hdac9, HDAC9 <100) ,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\noutliers above 100 are removed",
col = "Plaque_Vulnerability_Index",
fill = "Plaque_Vulnerability_Index",
palette = "npg",
add = "boxplot", error.plot = "crossbar") +
stat_compare_means(label = "p.format", method = "kruskal.test",
label.x = 1, label.y = 50) +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.Boxplot.outlier_above_100_removed.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
facet.by = "Plaque_Vulnerability_Index",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter",
add.params = list(size = 2, jitter = 0.2)) +
stat_compare_means(label = "p.format", method = "kruskal.test") +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.FacetByPlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9_max100",
facet.by = "Plaque_Vulnerability_Index",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter",
add.params = list(size = 2, jitter = 0.2)) +
stat_compare_means(label = "p.format", method = "kruskal.test") +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.FacetByPlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9_max100",
xlab = "Plaque vulnerability index by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())Saving 12 x 8 in image
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9_max100",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9_max100",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imageModel 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of the plaque vulnerability indez as a function of plaque target(s) levels.
TRAITS.TARGET.RANK.extra = c("HDAC9")
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
PROTEIN = TRAITS.TARGET.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
# currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
# table(currentDF$ORdate_year)
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
# fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
# data = currentDF,
# Hess = TRUE)
fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of HDAC9.
- processing Plaque_Vulnerability_IndexWarning: NaNs producedCall:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_epoch, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.0069184 0.0026273 2.633
Age 0.0124750 0.0007876 15.840
Gendermale 0.5460677 0.0029432 185.538
ORdate_epoch -0.0004384 NaN NaN
Intercepts:
Value Std. Error t value
0|1 -7.1513 0.0008 -8774.8479
1|2 -5.6841 0.0194 -292.8094
2|3 -4.4752 0.1029 -43.4887
3|4 -2.8315 0.1363 -20.7744
4|5 -1.5179 0.1398 -10.8560
Residual Deviance: 1961.92
AIC: 1979.92 Warning: NaNs produced Value Std. Error t value p value
currentDF[, PROTEIN] 0.006918357 0.0026272954 2.633262 8.456907e-03
Age 0.012475038 0.0007875536 15.840240 1.641938e-56
Gendermale 0.546067650 0.0029431603 185.537858 0.000000e+00
ORdate_epoch -0.000438398 NaN NaN NaN
0|1 -7.151291599 0.0008149761 -8774.847869 0.000000e+00
1|2 -5.684099685 0.0194122864 -292.809388 0.000000e+00
2|3 -4.475156538 0.1029038329 -43.488725 0.000000e+00
3|4 -2.831470035 0.1362958897 -20.774435 7.372715e-96
4|5 -1.517922118 0.1398234792 -10.855989 1.867744e-27Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
PROTEIN = TRAITS.TARGET.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
# currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
# data = currentDF,
# Hess = TRUE)
fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of HDAC9.
- processing Plaque_Vulnerability_IndexWarning: NaNs producedCall:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.007791 2.857e-03 2.727e+00
Age 0.012450 5.885e-03 2.116e+00
Gendermale 0.538602 2.577e-03 2.090e+02
ORdate_epoch -0.000541 NaN NaN
Hypertension.compositeyes -0.049872 8.558e-04 -5.828e+01
DiabetesStatusDiabetes -0.105116 3.196e-04 -3.289e+02
SmokerStatusEx-smoker 0.058168 1.202e-03 4.839e+01
SmokerStatusNever smoked 0.289037 7.098e-04 4.072e+02
Med.Statin.LLDyes 0.062784 1.284e-03 4.891e+01
Med.all.antiplateletyes 0.090098 1.159e-03 7.774e+01
GFR_MDRD 0.001444 3.393e-03 4.256e-01
BMI -0.043742 1.625e-02 -2.691e+00
MedHx_CVDNo -0.221798 2.329e-03 -9.525e+01
stenose50-70% -0.521282 2.560e-04 -2.036e+03
stenose70-90% -0.901200 2.429e-03 -3.711e+02
stenose90-99% -1.283931 2.446e-03 -5.250e+02
stenose100% (Occlusion) -1.719726 6.903e-05 -2.491e+04
stenose50-99% -28.618763 3.029e-11 -9.449e+11
Intercepts:
Value Std. Error t value
0|1 -1.061660e+01 5.000000e-04 -2.090716e+04
1|2 -9.111700e+00 2.240000e-02 -4.075256e+02
2|3 -7.888900e+00 1.129000e-01 -6.986070e+01
3|4 -6.218700e+00 1.488000e-01 -4.180540e+01
4|5 -4.890200e+00 1.529000e-01 -3.197420e+01
Residual Deviance: 1670.926
AIC: 1716.926 Warning: NaNs produced Value Std. Error t value p value
currentDF[, PROTEIN] 7.791379e-03 2.857069e-03 2.727053e+00 6.390278e-03
Age 1.244976e-02 5.884999e-03 2.115507e+00 3.438674e-02
Gendermale 5.386019e-01 2.576587e-03 2.090369e+02 0.000000e+00
ORdate_epoch -5.409604e-04 NaN NaN NaN
Hypertension.compositeyes -4.987198e-02 8.557950e-04 -5.827562e+01 0.000000e+00
DiabetesStatusDiabetes -1.051157e-01 3.195562e-04 -3.289428e+02 0.000000e+00
SmokerStatusEx-smoker 5.816763e-02 1.202102e-03 4.838828e+01 0.000000e+00
SmokerStatusNever smoked 2.890373e-01 7.098101e-04 4.072037e+02 0.000000e+00
Med.Statin.LLDyes 6.278367e-02 1.283646e-03 4.891042e+01 0.000000e+00
Med.all.antiplateletyes 9.009767e-02 1.158925e-03 7.774248e+01 0.000000e+00
GFR_MDRD 1.444008e-03 3.392560e-03 4.256395e-01 6.703705e-01
BMI -4.374247e-02 1.625429e-02 -2.691134e+00 7.120958e-03
MedHx_CVDNo -2.217979e-01 2.328509e-03 -9.525319e+01 0.000000e+00
stenose50-70% -5.212822e-01 2.560112e-04 -2.036169e+03 0.000000e+00
stenose70-90% -9.012001e-01 2.428538e-03 -3.710875e+02 0.000000e+00
stenose90-99% -1.283931e+00 2.445602e-03 -5.249956e+02 0.000000e+00
stenose100% (Occlusion) -1.719726e+00 6.903322e-05 -2.491158e+04 0.000000e+00
stenose50-99% -2.861876e+01 3.028882e-11 -9.448624e+11 0.000000e+00
0|1 -1.061656e+01 5.077954e-04 -2.090716e+04 0.000000e+00
1|2 -9.111703e+00 2.235861e-02 -4.075256e+02 0.000000e+00
2|3 -7.888915e+00 1.129235e-01 -6.986070e+01 0.000000e+00
3|4 -6.218676e+00 1.487528e-01 -4.180543e+01 0.000000e+00
4|5 -4.890208e+00 1.529422e-01 -3.197422e+01 2.489339e-224Saving data for share
We also want to share the data with our collaborators. And provide some more graphs and summary statistics too.
summary(AERNASE.clin.hdac9$HDAC9) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 11.00 21.00 28.32 37.00 449.00 ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
color = "Gender", fill = "Gender",
add = "mean", add_density = TRUE,
xlab = "HDAC9 (normalized expression)",
palette = "npg")Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imageggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
color = "Plaque_Vulnerability_Index", fill = "Plaque_Vulnerability_Index",
facet.by = "Plaque_Vulnerability_Index",
add = "mean", add_density = TRUE,
xlab = "HDAC9 (normalized expression)",
palette = "npg")Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.FacetbyPVI.pdf"), plot = last_plot())Saving 12 x 8 in imageggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
color = "Plaque_Vulnerability_Index", fill = "Plaque_Vulnerability_Index",
# facet.by = "Plaque_Vulnerability_Index",
add = "mean", add_density = TRUE,
xlab = "HDAC9 (normalized expression)",
palette = "npg")Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.byPVI.pdf"), plot = last_plot())Saving 12 x 8 in imageggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
fill = "black", rug = TRUE,
add = "mean", add_density = TRUE,
xlab = "HDAC9 (normalized expression)",
palette = "npg")Warning: Using `bins = 30` by default. Pick better value with the argument `bins`.Warning: `geom_vline()`: Ignoring `mapping` because `xintercept` was provided.Warning: `geom_vline()`: Ignoring `data` because `xintercept` was provided.
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.pdf"), plot = last_plot())Saving 12 x 8 in imageAERNASE.clin.hdac9.forSHARE <- subset(AERNASE.clin.hdac9, select = c("STUDY_NUMBER", "Age", "Gender",
"HDAC9", "HDAC9_max100", "Plaque_Vulnerability_Index"))saveRDS(AERNASE.clin.hdac9.forSHARE, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.hdac9.forSHARE.rds"))
fwrite(AERNASE.clin.hdac9.forSHARE, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.hdac9.forSHARE.txt"),
sep = "\t",
quote = FALSE,
na = "NA",
verbose = TRUE, showProgress = TRUE, nThread = 8)No list columns are present. Setting sep2='' otherwise quote='auto' would quote fields containing sep2.
Column writers: 12 5 13 3 5 13
args.doRowNames=0 args.rowNames=0 doQuote=0 args.nrow=623 args.ncol=6 eolLen=1
maxLineLen=173. Found in 0.000s
Writing bom (false), yaml (0 characters) and column names (true) ... done in 0.000s
Writing 623 rows in 1 batches of 623 rows (each buffer size 8MB, showProgress=1, nth=1)Plotting HDAC9 vs Fat 10 perc. in the plaque
# Global test
compare_means(HDAC9 ~ Fat.bin_10, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9",
xlab = "Fat <10% vs >10%",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_10",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <10% vs >10%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Fat.bin_10, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9",
xlab = "Fat <10% vs >10% by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Fat <10% vs >10% by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Fat.bin_10, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9",
xlab = "Fat <10% vs >10% by year of surgery",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_10",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Fat <10% vs >10% by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10_Facet_byYear.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Fat.bin_10, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9",
xlab = "Fat <10% vs >10% by year of surgery and gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Fat <10% vs >10% by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10_Facet_byYear.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Fat.bin_10, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9_max100",
xlab = "Fat <10% vs >10%",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_10",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <10% vs >10%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.Fat.bin_10.pdf"), plot = last_plot())Saving 12 x 8 in imagePlotting HDAC9 vs Fat 40 perc. in the plaque
# Global test
compare_means(HDAC9 ~ Fat.bin_40, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9",
xlab = "Fat <40% vs >40%",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_40",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <40% vs >40%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Fat.bin_40, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9",
xlab = "Fat <40% vs >40% by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Fat <40% vs >40% by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Fat.bin_40, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9",
xlab = "Fat <40% vs >40% by year of surgery",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_40",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Fat <40% vs >40% by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40_Facet_byYear.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Fat.bin_40, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9",
xlab = "Fat <40% vs >40% by year of surgery and gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Fat <40% vs >40% by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40_Facet_byYear.byGender.pdf"), plot = last_plot())Saving 12 x 8 in image# Global test
compare_means(HDAC9_max100 ~ Fat.bin_40, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9_max100",
xlab = "Fat <40% vs >40%",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_40",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <40% vs >40%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.Fat.bin_40.pdf"), plot = last_plot())Saving 12 x 8 in imagePlotting HDAC9 vs IPH in the plaque
# Global test
compare_means(HDAC9 ~ IPH.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9",
xlab = "Intraplaque hemorrhage (no vs. yes)",
ylab = "HDAC9 (normalized expression)\n",
color = "IPH.bin",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "IPH")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ IPH.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9",
xlab = "Intraplaque hemorrhage (no vs. yes) by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "IPH by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ IPH.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9",
xlab = "Intraplaque hemorrhage (no vs. yes) by year of surgery",
ylab = "HDAC9 (normalized expression)\n",
color = "IPH.bin",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "IPH by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin_Facet_byYear.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ IPH.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9",
xlab = "Intraplaque hemorrhage (no vs. yes) by year of surgery and gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "IPH by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin_Facet_byYear.byGender.pdf"), plot = last_plot())Saving 12 x 8 in image# Global test
compare_means(HDAC9_max100 ~ IPH.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9_max100",
xlab = "Intraplaque hemorrhage (no vs. yes)",
ylab = "HDAC9 (normalized expression)\n",
color = "IPH.bin",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "IPH")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.IPH.bin.pdf"), plot = last_plot())Saving 12 x 8 in imagePlotting HDAC9 vs Calcification in the plaque
# Global test
compare_means(HDAC9 ~ Calc.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9",
xlab = "Calcification (no/minor vs. moderate/heavy)",
ylab = "HDAC9 (normalized expression)\n",
color = "Calc.bin",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Calcification")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Calc.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9",
xlab = "Calcification (no/minor vs. moderate/heavy) by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Calcification by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Calc.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9",
xlab = "Calcification (no/minor vs. moderate/heavy) by year of surgery",
ylab = "HDAC9 (normalized expression)\n",
color = "Calc.bin",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Calcification by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin_Facet_byYear.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Calc.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9",
xlab = "Calcification (no/minor vs. moderate/heavy) by year of surgery and gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Calcification by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin_Facet_byYear.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9_max100 ~ Calc.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9_max100",
xlab = "Calcification (no/minor vs. moderate/heavy)",
ylab = "HDAC9 (normalized expression)\n",
color = "Calc.bin",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Calcification")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.Calc.bin.pdf"), plot = last_plot())Saving 12 x 8 in imageSession information
Version: v1.0.6
Last update: 2023-06-06
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to analyse HDAC9 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.0.6 Added maximum counts to HDAC9 in many graphs.
* v1.0.5 Fixed forest plot and alternative boxplot for symptoms.
* v1.0.4 Made histogram of PVI. Exported HDAC9 and PVI data.
* v1.0.3 Small adaptations to PVI-plots.
* v1.0.2 Changed the PVI-plot.
* v1.0.1 Added figures on fat in the plaque.
* v1.0.0 Inital version.sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin22.4.0 (64-bit)
Running under: macOS Ventura 13.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/r/4.3.0_1/lib/R/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Amsterdam
tzcode source: internal
attached base packages:
[1] grid tools stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] UpSetR_1.4.0 forestplot_3.1.1 abind_1.4-5 checkmate_2.2.0
[5] pheatmap_1.0.12 BlandAltmanLeh_0.3.1 eeptools_1.2.4 pander_0.6.5
[9] R.utils_2.12.2 R.oo_1.25.0 R.methodsS3_1.8.2 credentials_1.3.2
[13] ggcorrplot_0.1.4.999 openxlsx_4.2.5.2 ggpubr_0.6.0 tableone_0.13.2
[17] labelled_2.11.0 sjPlot_2.8.14 sjlabelled_1.2.0 haven_2.5.2
[21] devtools_2.4.5 usethis_2.1.6 MASS_7.3-60 DT_0.27
[25] knitr_1.42 lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[29] purrr_1.0.1 tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
[33] data.table_1.14.8 naniar_1.0.0 tidyr_1.3.0 dplyr_1.1.2
[37] optparse_1.7.3 readr_2.1.4 rmarkdown_2.21 worcs_0.1.10
[41] SummarizedExperiment_1.30.1 Biobase_2.60.0 GenomicRanges_1.52.0 GenomeInfoDb_1.36.0
[45] IRanges_2.34.0 S4Vectors_0.38.1 BiocGenerics_0.46.0 MatrixGenerics_1.12.0
[49] matrixStats_0.63.0
loaded via a namespace (and not attached):
[1] splines_4.3.0 later_1.3.1 bitops_1.0-7 prereg_0.6.0 lifecycle_1.0.3
[6] rstatix_0.7.2 gert_1.9.2 processx_3.8.1 lattice_0.21-8 crosstalk_1.2.0
[11] insight_0.19.1 vcd_1.4-11 backports_1.4.1 survey_4.2-1 magrittr_2.0.3
[16] sass_0.4.6 jquerylib_0.1.4 yaml_2.3.7 remotes_2.4.2 httpuv_1.6.11
[21] zip_2.3.0 askpass_1.1 sp_1.6-0 sessioninfo_1.2.2 pkgbuild_1.4.0
[26] RColorBrewer_1.1-3 DBI_1.1.3 minqa_1.2.5 multcomp_1.4-23 pkgload_1.3.2
[31] zlibbioc_1.46.0 RCurl_1.98-1.12 TH.data_1.1-2 sandwich_3.0-2 GenomeInfoDbData_1.2.10
[36] arm_1.13-1 performance_0.10.3 codetools_0.2-19 getopt_1.20.3 DelayedArray_0.26.2
[41] rticles_0.24 tidyselect_1.2.0 ggeffects_1.2.2 farver_2.1.1 lme4_1.1-33
[46] jsonlite_1.8.4 ellipsis_0.3.2 survival_3.5-5 emmeans_1.8.6 systemfonts_1.0.4
[51] ragg_1.2.5 Rcpp_1.0.10 glue_1.6.2 gridExtra_2.3 xfun_0.39
[56] ranger_0.15.1 withr_2.5.0 fastmap_1.1.1 mitools_2.4 boot_1.3-28.1
[61] fansi_1.0.4 openssl_2.0.6 callr_3.7.3 digest_0.6.31 timechange_0.2.0
[66] R6_2.5.1 mime_0.12 estimability_1.4.1 visdat_0.6.0 textshaping_0.3.6
[71] colorspace_2.1-0 ggsci_3.0.0 utf8_1.2.3 generics_0.1.3 renv_0.17.3
[76] prettyunits_1.1.1 htmlwidgets_1.6.2 S4Arrays_1.0.1 pkgconfig_2.0.3 gtable_0.3.3
[81] lmtest_0.9-40 XVector_0.40.0 sys_3.4.1 htmltools_0.5.5 carData_3.0-5
[86] profvis_0.3.8 scales_1.2.1 rstudioapi_0.14 reshape2_1.4.4 tzdb_0.4.0
[91] curl_5.0.0 coda_0.19-4 nlme_3.1-162 nloptr_2.0.3 cachem_1.0.8
[96] zoo_1.8-12 miniUI_0.1.1.1 foreign_0.8-84 pillar_1.9.0 vctrs_0.6.2
[101] urlchecker_1.0.1 promises_1.2.0.1 car_3.1-2 xtable_1.8-4 evaluate_0.21
[106] maptools_1.1-6 tinytex_0.45 mvtnorm_1.1-3 cli_3.6.1 compiler_4.3.0
[111] rlang_1.1.1 crayon_1.5.2 ggsignif_0.6.4 modelr_0.1.11 labeling_0.4.2
[116] ps_1.7.5 plyr_1.8.8 sjmisc_2.8.9 fs_1.6.2 stringi_1.7.12
[121] munsell_0.5.0 gh_1.4.0 bayestestR_0.13.1 Matrix_1.5-4 sjstats_0.18.2
[126] hms_1.1.3 shiny_1.7.4 broom_1.0.4 memoise_2.0.1 bslib_0.4.2 Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))| © 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | vanderlaan.science. | |
---
title: "Additional Figures"
author: '[Sander W. van der Laan, PhD](https://vanderlaan.science) | s.w.vanderlaan@gmail.com.'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
    highlight: tango
mainfont: Helvetica
subtitle: Accompanying 'Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy, an analysis in the Athero-EXPRESS Biobank.'
editor_options:
  chunk_output_type: inline
bibliography: references.bib
knit: worcs::cite_all
---
# General Setup

```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
source("scripts/local.system.R")

```

```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```

```{r Setting: loading_packages, message=FALSE, warning=FALSE}
source(paste0(PROJECT_loc, "/scripts/pack01.packages.R"))

```

```{r Setting: Colors, include = FALSE}
source(paste0(PROJECT_loc, "/scripts/colors.R"))
```


# Background

This notebook contains additional figures of the project "Plaque expression levels of _HDAC9_ in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank.".


# Loading data

```{r Loading project data}
# load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
load(paste0(PROJECT_loc, "/20220319.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
```

```{r}
source("/Users/slaan3/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9/scripts/local.system.R")
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

```


# Fix some variables

We need to get the 'conventional unit' versions of cholesterols.

```{r}
AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        "risk614", 
                                                        "LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
```


# Fix counts

We limit the maximum count at 100. 
```{r}
# ?ggpubr::ggboxplot()
# compare_means(LRCH1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
# ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup))

cat("Get summary statistics for target:\n")
summary(AERNASE.clin.hdac9$HDAC9)

cat("\nCount number of values > 100 for target:\n")
sum(AERNASE.clin.hdac9$HDAC9 > 100)

cat("\nSetting values > 100 for target to 100.\n")
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(HDAC9_max100 = ifelse(HDAC9 > 100, 100, HDAC9)) 

cat("Get summary statistics for target after fixing values:\n")
summary(AERNASE.clin.hdac9$HDAC9_max100)
```

# Additional figures

## Age and sex
We want to create per-age-group figures median ± interquartile range. 

- Box and Whisker plot for target(s) plaque levels by sex.
- Box and Whisker plot for target(s) plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).


```{r per Sex}

# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ Gender,  data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("Gender"),
                  y = "HDAC9", 
                  xlab = "gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Gender.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Gender,  data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("Gender"),
                  y = "HDAC9_max100", 
                  xlab = "gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Gender.pdf"), plot = last_plot())

```


```{r AgeGroups}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$AgeGroupSex)

```

Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.

```{r per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ AgeGroup,  data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("AgeGroup"),
                  y = "HDAC9", 
                  xlab = "Age groups (years)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup.pdf"), plot = last_plot())

compare_means(HDAC9 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("AgeGroup"),
                  y = "HDAC9", 
                  xlab = "Age groups (years) per gender",
                  ylab = "HDAC9 (normalized expression",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup_perGender.pdf"), plot = last_plot())

# ?ggpubr::ggboxplot()
compare_means(HDAC9_max100 ~ AgeGroup,  data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("AgeGroup"),
                  y = "HDAC9_max100", 
                  xlab = "Age groups (years)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.AgeGroup.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("AgeGroup"),
                  y = "HDAC9_max100", 
                  xlab = "Age groups (years) per gender",
                  ylab = "HDAC9 (normalized expression",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.AgeGroup_perGender.pdf"), plot = last_plot())

```


## Hypertension & blood pressure
We want to create figures of target(s) levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs. 

- Box and Whisker plot for target(s) plaque levels by hypertension group (no, yes)
- Box and Whisker plot for target(s) plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)


```{r BloodPressure}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AERNASE.clin.hdac9$SBPGroup, AERNASE.clin.hdac9$Gender)

```

Now we can draw some graphs of plaque target(s) levels per sex and hypertension/blood pressure group as median ± interquartile range.

```{r per BloodPressure per Sex}
compare_means(HDAC9 ~ SBPGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HDAC9", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.selfreport, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9", 
                  xlab = "Self-reported hypertension",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.drugs, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HDAC9", 
                  xlab = "Hypertension medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.HypertensionDrugs.pdf"), plot = last_plot())



compare_means(HDAC9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HDAC9", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HDAC9", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())



compare_means(HDAC9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "HDAC9", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())

```

```{r per BloodPressure per Sex HDAC9_max100}
compare_means(HDAC9_max100 ~ SBPGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HDAC9_max100", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Hypertension.selfreport, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9_max100", 
                  xlab = "Self-reported hypertension",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Hypertension.drugs, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HDAC9_max100", 
                  xlab = "Hypertension medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.HypertensionDrugs.pdf"), plot = last_plot())



compare_means(HDAC9_max100 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HDAC9_max100", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup_byGender.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9_max100", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension_byGender.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HDAC9_max100", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())



compare_means(HDAC9_max100 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "HDAC9_max100", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9_max100", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())

```


## Hypercholesterolemia & LDL levels
We want to create figures of target(s) levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs. 

- Box and Whisker plot for target(s) plaque levels by hypercholesterolemia (`risk614`) group (no, yes)
- Box and Whisker plot for target(s) plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for target(s) plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)

```{r LDLGroups}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AERNASE.clin.hdac9$LDLGroup, AERNASE.clin.hdac9$Gender)

```


```{r Fix Hypercholesterolemia, message=FALSE, warning=FALSE}
require(sjlabelled)

AERNASE.clin.hdac9$risk614 <- to_factor(AERNASE.clin.hdac9$risk614)

# Fix plaquephenotypes
attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"Hypercholesterolemia"] <- NA
# AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "missing value"] <- NA
# AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "no"] <- "no"
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "yes"] <- "yes"
detach(AERNASE.clin.hdac9)

table(AERNASE.clin.hdac9$risk614, AERNASE.clin.hdac9$Hypercholesterolemia)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

Now we can draw some graphs of plaque target(s) levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.

```{r per Hypercholesterolemia per Sex}

compare_means(HDAC9 ~ LDLGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HDAC9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups.pdf"), plot = last_plot())

compare_means(HDAC9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HDAC9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byGender.pdf"), plot = last_plot())



compare_means(HDAC9 ~ Hypercholesterolemia, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())


compare_means(HDAC9 ~ Med.Statin.LLD, data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HDAC9", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HDAC9", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())




compare_means(HDAC9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "HDAC9", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())


```


```{r per Hypercholesterolemia per Sex HDAC9_max100}

compare_means(HDAC9_max100 ~ LDLGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HDAC9_max100", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HDAC9_max100", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byGender.pdf"), plot = last_plot())



compare_means(HDAC9_max100 ~ Hypercholesterolemia, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9_max100", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypercholesterolemia.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9_max100", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())


compare_means(HDAC9_max100 ~ Med.Statin.LLD, data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HDAC9_max100", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Med.Statin.LLD.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HDAC9_max100", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())




compare_means(HDAC9_max100 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "HDAC9_max100", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9_max100", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())


```

## Kidney function (eGFR)
We want to create figures of target(s) levels stratified by kidney function. 

- Box and Whisker plot for target(s) plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for target(s) plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)

```{r EGFR}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$Gender)

table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$KDOQI)

```

Now we can draw some graphs of plaque target(s) levels per sex and kidney function group as median ± interquartile range.


```{r per EGFR per Sex}

# Global test

compare_means(HDAC9 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR.pdf"), plot = last_plot())

compare_means(HDAC9 ~ eGFRGroup, group.by = "Gender",  data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ KDOQI, data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HDAC9", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI.pdf"), plot = last_plot())

compare_means(HDAC9 ~ KDOQI, group.by = "Gender",   data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HDAC9", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ eGFRGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "HDAC9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_KDOQI.pdf"), plot = last_plot())

```

```{r per EGFR per Sex HDAC9_max100}

# Global test

compare_means(HDAC9_max100 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9_max100", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ eGFRGroup, group.by = "Gender",  data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9_max100", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR_byGender.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ KDOQI, data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HDAC9_max100", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.KDOQI.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ KDOQI, group.by = "Gender",   data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HDAC9_max100", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.KDOQI_byGender.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ eGFRGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9_max100", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "HDAC9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.EGFR_KDOQI.pdf"), plot = last_plot())

```

## BMI
We want to create figures of target(s) levels stratified by BMI. 

- Box and Whisker plot for target(s) plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for target(s) plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)

```{r BMI}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AERNASE.clin.hdac9$BMI_US <- as_factor(AERNASE.clin.hdac9$BMI_US)
# AERNASE.clin.hdac9$BMI_WHO <- as_factor(AERNASE.clin.hdac9$BMI_WHO)
# table(AERNASE.clin.hdac9$BMI_WHO, AERNASE.clin.hdac9$BMI_US)

table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$BMI_WHO)

```

Now we can draw some graphs of plaque MCP1 levels per sex and age group as median ± interquartile range.


```{r per BMI per Sex}

# Global test
compare_means(HDAC9 ~ BMIGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HDAC9", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI.pdf"), plot = last_plot())

compare_means(HDAC9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HDAC9", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ BMIGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "HDAC9", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "HDAC9 (normalized expression)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byWHO.pdf"), plot = last_plot())

```


```{r per BMI per Sex HDAC9_max100}

# Global test
compare_means(HDAC9_max100 ~ BMIGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HDAC9_max100", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.BMI.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HDAC9_max100", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.BMI_byGender.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ BMIGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "HDAC9_max100", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "HDAC9 (normalized expression)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.BMI_byWHO.pdf"), plot = last_plot())

```



## Diabetes
We want to create figures of target(s) levels stratified by type 2 diabetes. 

- Box and Whisker plot for target(s) plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.


```{r per Diabetes per Sex}

# Global test
compare_means(HDAC9 ~ DiabetesStatus,  data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "HDAC9", 
                  xlab = "Diabetes status",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Diabetes.pdf"), plot = last_plot())

compare_means(HDAC9 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "HDAC9", 
                  xlab = "Diabetes status per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Diabetes_byGender.pdf"), plot = last_plot())


```


```{r per Diabetes per Sex HDAC9_max100}

# Global test
compare_means(HDAC9_max100 ~ DiabetesStatus,  data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "HDAC9_max100", 
                  xlab = "Diabetes status",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Diabetes.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "HDAC9_max100", 
                  xlab = "Diabetes status per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Diabetes_byGender.pdf"), plot = last_plot())


```


## Smoking
We want to create figures of target(s) levels stratified by smoking. 

- Box and Whisker plot for target(s) plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.


```{r per Smoking per Sex}

# Global test
compare_means(HDAC9 ~ SmokerStatus,  data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HDAC9", 
                  xlab = "Smoker status",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Smoking.pdf"), plot = last_plot())

compare_means(HDAC9 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HDAC9", 
                  xlab = "Smoker status per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Smoking_byGender.pdf"), plot = last_plot())

```

```{r per Smoking per Sex HDAC9_max100}

# Global test
compare_means(HDAC9_max100 ~ SmokerStatus,  data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HDAC9_max100", 
                  xlab = "Smoker status",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Smoking.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HDAC9_max100", 
                  xlab = "Smoker status per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Smoking_byGender.pdf"), plot = last_plot())

```

## Stenosis
We want to create figures of target(s) levels stratified by stenosis grade. 

- Box and Whisker plot for target(s) plaque levels by stenosis grade group (<70, 70-89, 90+)

```{r Stenosis}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AERNASE.clin.hdac9$StenoticGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$stenose, AERNASE.clin.hdac9$StenoticGroup)

```

Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.

```{r per Stenosis per Sex}

# Global test
compare_means(HDAC9 ~ StenoticGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HDAC9", 
                  xlab = "Stenotic grade",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Stenosis.pdf"), plot = last_plot())

compare_means(HDAC9 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HDAC9", 
                  xlab = "Stenotic grade per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Stenosis_byGender.pdf"), plot = last_plot())

```

```{r per Stenosis per Sex HDAC9_max100}

# Global test
compare_means(HDAC9_max100 ~ StenoticGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HDAC9_max100", 
                  xlab = "Stenotic grade",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Stenosis.pdf"), plot = last_plot())

compare_means(HDAC9_max100 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HDAC9_max100", 
                  xlab = "Stenotic grade per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".max100.plaque.Stenosis_byGender.pdf"), plot = last_plot())

```



## Symptoms
We want to create per-symptom figures. 

```{r SymptomGroups}
library(dplyr)

table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$AsymptSympt2G)
table(AERNASE.clin.hdac9$Gender, AERNASE.clin.hdac9$AsymptSympt2G)
table(AERNASE.clin.hdac9$AsymptSympt2G)

```

Now we can draw some graphs of plaque target(s) levels per symptom group as median ± interquartile range.

```{r per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

compare_means(HDAC9 ~ AsymptSympt2G, data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "AsymptSympt2G", y = "HDAC9",
                  title = "HDAC9 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "HDAC9 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)
```

```{r per SymptomGroups by Gender}
compare_means(HDAC9 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "AsymptSympt2G", y = "HDAC9",
                  title = "HDAC9 (normalized expression) levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())

rm(p1)

```

### Alternative graph
```{r}
# ?ggpubr::ggboxplot()
# compare_means(LRCH1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
# ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup))

cat("Get summary statistics for target:\n")
summary(AERNASE.clin.hdac9$HDAC9)

cat("\nCount number of values > 100 for target:\n")
sum(AERNASE.clin.hdac9$HDAC9 > 100)

cat("\nSetting values > 100 for target to 100.\n")
temp <- AERNASE.clin.hdac9 %>% 
  filter(!is.na(AsymptSympt2G))

temp$HDAC9[temp$HDAC9 > 100] <- 100

cat("Get summary statistics for target after fixing values:\n")
summary(temp$HDAC9)

```


```{r}
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

compare_means(HDAC9 ~ AsymptSympt2G, data = temp, method = "kruskal.test")

p1 <- ggpubr::ggboxplot(temp, 
                  x = "AsymptSympt2G", y = "HDAC9",
                  title = "HDAC9 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "HDAC9 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "jitter", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.pdf"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.ps"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.png"), plot = last_plot())

rm(p1, temp)
```



## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
# model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
# model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))

model1_target <- read.xlsx(paste0(OUT_loc, "/20230531.AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/20230531.AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))

model1_target$model <- "univariate"
model2_target$model <- "multivariate"

models_target <- rbind(model1_target, model2_target)
models_target

```

Forest plots.

```{r forestplot plaque, experiment 2}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_target$OR[models_target$Predictor=="HDAC9"]),
                  low = c(models_target$low95CI[models_target$Predictor=="HDAC9"]),
                  high = c(models_target$up95CI[models_target$Predictor=="HDAC9"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Normalized HDAC9 expression") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.forest.pdf"), plot = fp)

rm(fp)
```


## HDAC9 vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the target(s)` plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data

```{r}
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"

# fix names
names(AERNASE.clin.hdac9)[names(AERNASE.clin.hdac9) == "IL6"] <- "IL6rna"

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")


AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        cytokines,
                                                        metalloproteinases)), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

```


```{r HDAC9 vs Cytokines INRT, paged.print=TRUE}

proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))

# make variables numerics()
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AERNASE.clin.hdac9 <-  AERNASE.clin.hdac9 %>% mutate(AERNASE.clin.hdac9[,var.temp] == replace(AERNASE.clin.hdac9[,var.temp], AERNASE.clin.hdac9[,var.temp]==0, NA))

  AERNASE.clin.hdac9[,var.temp][AERNASE.clin.hdac9[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AERNASE.clin.hdac9[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AERNASE.clin.hdac9[,var.temp])),").\n"))
  
  AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AERNASE.clin.hdac9[,var.temp] - mean(AERNASE.clin.hdac9[,var.temp], na.rm = TRUE))/sd(AERNASE.clin.hdac9[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AERNASE.clin.hdac9[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.hdac9[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AERNASE.clin.hdac9[,var.temp.rank] <- NULL
  names(AERNASE.clin.hdac9)[names(AERNASE.clin.hdac9) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r HDAC9 vs Cytokines Histograms}
proteins_of_interest_rank_target <- c("HDAC9", proteins_of_interest_rank)

proteins_of_interest_target <- c("HDAC9", proteins_of_interest)

for(PROTEIN in proteins_of_interest_target){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin.hdac9, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN in proteins_of_interest_rank_target){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin.hdac9, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between target(s) and 28 other cytokines. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r MCP1 vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin.hdac9, 
                          select = c(proteins_of_interest_rank_target)
                                    )

# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)

corr_biomarkers.rank <- round(cor(matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_target <- c("HDAC9 (RNA)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r MCP1 vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 16,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())

rm(p1)

```

While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of target(s)` with other cytokines only.

```{r MCP1 vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HDAC9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
library(dplyr)
library(tidyr)
library(ggplot2)

p1 <- ggpubr::ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = "Spearman's rho", # expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 1.25
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) 

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.pdf"), plot = last_plot())
rm(p1)


```

Another version - probably not good. 
```{r MCP1 vs Cytokines dotchart}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HDAC9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY", #fill = "CytokineY",                              # Color by groups
           palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           ylab = "log10(P)", # expression(log[10]~"("~italic(p)~")-value"),
           # ylim = c(0, 9),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 16,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 12, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.dotchart.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.dotchart.pdf"), plot = last_plot())

rm(temp, p1)

```


## HDAC9 vs. cytokines plaque levels

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plaque target(s) levels.

```{r CrossSec: Cytokines - linear regression MODEL1 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plaque target(s) levels.

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

## HDAC9 levels vs. vulnerability index


Here we plot the levels of inverse-rank normal transformed target(s) plaque levels from experiment 1 and 2 to the `Plaque vulnerability index`. 

```{r Fix ORyearGroup, message=FALSE, warning=FALSE}
library(sjlabelled)

AERNASE.clin.hdac9$yeartemp <- as.numeric(year(AERNASE.clin.hdac9$dateok))

attach(AERNASE.clin.hdac9)

AERNASE.clin.hdac9[,"ORyearGroup"] <- NA
AERNASE.clin.hdac9$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin.hdac9$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin.hdac9)

table(AERNASE.clin.hdac9$ORyearGroup, AERNASE.clin.hdac9$ORdate_year)
```

### Visualisations

```{r per PlaqueVulnerabilityIndex}
# Global test
compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter", 
                  add.params = list(size = 2, jitter = 0.2)) +
  stat_compare_means(label = "p.format",  method = "kruskal.test") +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9_max100", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter", 
                  add.params = list(size = 2, jitter = 0.2)) +
  stat_compare_means(label = "p.format",  method = "kruskal.test") +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())
```


```{r }
# Global test
compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")

p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  col = "Plaque_Vulnerability_Index",
                  fill = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "median_iqr", error.plot = "upper_errorbar") +
  stat_compare_means(label = "p.format",  method = "kruskal.test",
                     label.x = 1, label.y = 50) +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.BarPlot.median_iqr.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")

p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9_max100", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  col = "Plaque_Vulnerability_Index",
                  fill = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "median_iqr", error.plot = "upper_errorbar") +
  stat_compare_means(label = "p.format",  method = "kruskal.test",
                     label.x = 1, label.y = 50) +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex.BarPlot.median_iqr.pdf"), plot = last_plot())

```


```{r}
p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  col = "Plaque_Vulnerability_Index",
                  fill = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "mean_se", error.plot = "upper_errorbar") +
  stat_compare_means(label = "p.format",  method = "kruskal.test",
                     label.x = 1, label.y = 50) +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.BarPlot.means_se.pdf"), plot = last_plot())

```

```{r}
p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9_max100", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  col = "Plaque_Vulnerability_Index",
                  fill = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "mean_se", error.plot = "upper_errorbar") +
  stat_compare_means(label = "p.format",  method = "kruskal.test",
                     label.x = 1, label.y = 50) +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex.BarPlot.means_se.pdf"), plot = last_plot())
```


```{r}
compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = subset(AERNASE.clin.hdac9, HDAC9 <100), method = "kruskal.test")

p1 <- ggpubr::ggboxplot(subset(AERNASE.clin.hdac9, HDAC9 <100) , 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\noutliers above 100 are removed",
                  col = "Plaque_Vulnerability_Index",
                  fill = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "boxplot", error.plot = "crossbar") +
  stat_compare_means(label = "p.format",  method = "kruskal.test",
                     label.x = 1, label.y = 50) +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.Boxplot.outlier_above_100_removed.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  facet.by = "Plaque_Vulnerability_Index",
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter", 
                  add.params = list(size = 2, jitter = 0.2)) +
  stat_compare_means(label = "p.format",  method = "kruskal.test") +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.FacetByPlaqueVulnerabilityIndex.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9_max100", 
                  facet.by = "Plaque_Vulnerability_Index",
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter", 
                  add.params = list(size = 2, jitter = 0.2)) +
  stat_compare_means(label = "p.format",  method = "kruskal.test") +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.FacetByPlaqueVulnerabilityIndex.pdf"), plot = last_plot())
```



```{r}
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9_max100", 
                  xlab = "Plaque vulnerability index by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())
```


```{r}

compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9_max100", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())
```



```{r}
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9_max100 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9_max100", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())
```


### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability indez as a function of plaque target(s) levels.

```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL1 RANK, paged.print=TRUE}
TRAITS.TARGET.RANK.extra = c("HDAC9")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, 
    #           Hess = TRUE)
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }


```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL2 RANK, paged.print=TRUE}

for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
    #           data  =  currentDF,
    #           Hess = TRUE)
    
    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
              data  =  currentDF,
              Hess = TRUE)
    
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

```

### Saving data for share

We also want to share the data with our collaborators. And provide some more graphs and summary statistics too.

```{r}
summary(AERNASE.clin.hdac9$HDAC9)
```


```{r}
ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
                    color = "Gender", fill = "Gender",
                    add = "mean", add_density = TRUE,
                    xlab = "HDAC9 (normalized expression)",
                    palette = "npg")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.byGender.pdf"), plot = last_plot())

ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
                    color = "Plaque_Vulnerability_Index", fill = "Plaque_Vulnerability_Index",
                    facet.by = "Plaque_Vulnerability_Index",
                    add = "mean", add_density = TRUE,
                    xlab = "HDAC9 (normalized expression)",
                    palette = "npg")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.FacetbyPVI.pdf"), plot = last_plot())

ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
                    color = "Plaque_Vulnerability_Index", fill = "Plaque_Vulnerability_Index",
                    # facet.by = "Plaque_Vulnerability_Index",
                    add = "mean", add_density = TRUE,
                    xlab = "HDAC9 (normalized expression)",
                    palette = "npg")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.byPVI.pdf"), plot = last_plot())


ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
                    fill = "black", rug = TRUE,
                    add = "mean", add_density = TRUE,
                    xlab = "HDAC9 (normalized expression)",
                    palette = "npg")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.pdf"), plot = last_plot())

```

```{r}
AERNASE.clin.hdac9.forSHARE <- subset(AERNASE.clin.hdac9, select = c("STUDY_NUMBER", "Age", "Gender", 
                                                                     "HDAC9", "HDAC9_max100", "Plaque_Vulnerability_Index"))
```


```{r}
saveRDS(AERNASE.clin.hdac9.forSHARE, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.hdac9.forSHARE.rds"))

fwrite(AERNASE.clin.hdac9.forSHARE, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.hdac9.forSHARE.txt"),
       sep = "\t",
       quote = FALSE,
       na = "NA", 
       verbose = TRUE, showProgress = TRUE, nThread = 8)
```


## Plotting HDAC9 vs Fat 10 perc. in the plaque

```{r}
# Global test
compare_means(HDAC9 ~ Fat.bin_10,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9", 
                  xlab = "Fat <10% vs >10%",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_10",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <10% vs >10%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Fat.bin_10, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9", 
                  xlab = "Fat <10% vs >10% by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Fat <10% vs >10% by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ Fat.bin_10, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9", 
                  xlab = "Fat <10% vs >10% by year of surgery",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_10",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Fat <10% vs >10% by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Fat.bin_10, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9", 
                  xlab = "Fat <10% vs >10% by year of surgery and gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Fat <10% vs >10% by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10_Facet_byYear.byGender.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9_max100 ~ Fat.bin_10,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9_max100", 
                  xlab = "Fat <10% vs >10%",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_10",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <10% vs >10%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.Fat.bin_10.pdf"), plot = last_plot())
```


## Plotting HDAC9 vs Fat 40 perc. in the plaque

```{r}
# Global test
compare_means(HDAC9 ~ Fat.bin_40,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9", 
                  xlab = "Fat <40% vs >40%",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_40",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <40% vs >40%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Fat.bin_40, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9", 
                  xlab = "Fat <40% vs >40% by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Fat <40% vs >40% by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ Fat.bin_40, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9", 
                  xlab = "Fat <40% vs >40% by year of surgery",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_40",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Fat <40% vs >40% by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Fat.bin_40, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9", 
                  xlab = "Fat <40% vs >40% by year of surgery and gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Fat <40% vs >40% by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40_Facet_byYear.byGender.pdf"), plot = last_plot())
```

```{r}
# Global test
compare_means(HDAC9_max100 ~ Fat.bin_40,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9_max100", 
                  xlab = "Fat <40% vs >40%",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_40",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <40% vs >40%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.Fat.bin_40.pdf"), plot = last_plot())
```



## Plotting HDAC9 vs IPH in the plaque

```{r}
# Global test
compare_means(HDAC9 ~ IPH.bin,  data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9", 
                  xlab = "Intraplaque hemorrhage (no vs. yes)",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "IPH.bin",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "IPH")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ IPH.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9", 
                  xlab = "Intraplaque hemorrhage (no vs. yes) by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "IPH by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ IPH.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9", 
                  xlab = "Intraplaque hemorrhage (no vs. yes) by year of surgery",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "IPH.bin",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "IPH by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ IPH.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9", 
                  xlab = "Intraplaque hemorrhage (no vs. yes) by year of surgery and gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "IPH by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin_Facet_byYear.byGender.pdf"), plot = last_plot())
```

```{r}
# Global test
compare_means(HDAC9_max100 ~ IPH.bin,  data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9_max100", 
                  xlab = "Intraplaque hemorrhage (no vs. yes)",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "IPH.bin",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "IPH")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.IPH.bin.pdf"), plot = last_plot())
```



## Plotting HDAC9 vs Calcification in the plaque

```{r}
# Global test
compare_means(HDAC9 ~ Calc.bin,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9", 
                  xlab = "Calcification (no/minor vs. moderate/heavy)",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Calc.bin",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Calcification")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Calc.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9", 
                  xlab = "Calcification (no/minor vs. moderate/heavy) by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Calcification by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ Calc.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9", 
                  xlab = "Calcification (no/minor vs. moderate/heavy) by year of surgery",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Calc.bin",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Calcification by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Calc.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9", 
                  xlab = "Calcification (no/minor vs. moderate/heavy) by year of surgery and gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Calcification by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin_Facet_byYear.byGender.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9_max100 ~ Calc.bin,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9_max100", 
                  xlab = "Calcification (no/minor vs. moderate/heavy)",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Calc.bin",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Calcification")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".max100.plaque.Calc.bin.pdf"), plot = last_plot())
```


# Session information

--------------------------------------------------------------------------------

    Version:      v1.0.6
    Last update:  2023-06-06
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse HDAC9 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.0.6 Added maximum counts to HDAC9 in many graphs.
    * v1.0.5 Fixed forest plot and alternative boxplot for symptoms.
    * v1.0.4 Made histogram of PVI. Exported HDAC9 and PVI data.
    * v1.0.3 Small adaptations to PVI-plots.
    * v1.0.2 Changed the PVI-plot.
    * v1.0.1 Added figures on fat in the plaque.
    * v1.0.0 Inital version.
    

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))
```

+-----------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | [vanderlaan.science](https://vanderlaan.science).</sup> |
+-----------------------------------------------------------------------------------------------------------------------------------------+


